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loretoparisi
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tatoeba-sentences

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train

The dataset viewer is not available for this split.

Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.

Error code:   FeaturesError
Exception:    ParserError
Message:      Error tokenizing data. C error: Expected 1 fields in line 80, saw 2

Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 323, in compute
                  compute_first_rows_from_parquet_response(
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 88, in compute_first_rows_from_parquet_response
                  rows_index = indexer.get_rows_index(
                File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 631, in get_rows_index
                  return RowsIndex(
                File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 512, in __init__
                  self.parquet_index = self._init_parquet_index(
                File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 529, in _init_parquet_index
                  response = get_previous_step_or_raise(
                File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 566, in get_previous_step_or_raise
                  raise CachedArtifactError(
              libcommon.simple_cache.CachedArtifactError: The previous step failed.
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 241, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2216, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1239, in _head
                  return _examples_to_batch(list(self.take(n)))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1389, in __iter__
                  for key, example in ex_iterable:
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1044, in __iter__
                  yield from islice(self.ex_iterable, self.n)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 282, in __iter__
                  for key, pa_table in self.generate_tables_fn(**self.kwargs):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/csv/csv.py", line 195, in _generate_tables
                  for batch_idx, df in enumerate(csv_file_reader):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1843, in __next__
                  return self.get_chunk()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1985, in get_chunk
                  return self.read(nrows=size)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/readers.py", line 1923, in read
                  ) = self._engine.read(  # type: ignore[attr-defined]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/pandas/io/parsers/c_parser_wrapper.py", line 234, in read
                  chunks = self._reader.read_low_memory(nrows)
                File "parsers.pyx", line 850, in pandas._libs.parsers.TextReader.read_low_memory
                File "parsers.pyx", line 905, in pandas._libs.parsers.TextReader._read_rows
                File "parsers.pyx", line 874, in pandas._libs.parsers.TextReader._tokenize_rows
                File "parsers.pyx", line 891, in pandas._libs.parsers.TextReader._check_tokenize_status
                File "parsers.pyx", line 2061, in pandas._libs.parsers.raise_parser_error
              pandas.errors.ParserError: Error tokenizing data. C error: Expected 1 fields in line 80, saw 2

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YAML Metadata Error: "license" must be one of [apache-2.0, mit, openrail, bigscience-openrail-m, creativeml-openrail-m, bigscience-bloom-rail-1.0, bigcode-openrail-m, afl-3.0, artistic-2.0, bsl-1.0, bsd, bsd-2-clause, bsd-3-clause, bsd-3-clause-clear, c-uda, cc, cc0-1.0, cc-by-2.0, cc-by-2.5, cc-by-3.0, cc-by-4.0, cc-by-sa-3.0, cc-by-sa-4.0, cc-by-nc-2.0, cc-by-nc-3.0, cc-by-nc-4.0, cc-by-nd-4.0, cc-by-nc-nd-3.0, cc-by-nc-nd-4.0, cc-by-nc-sa-2.0, cc-by-nc-sa-3.0, cc-by-nc-sa-4.0, cdla-sharing-1.0, cdla-permissive-1.0, cdla-permissive-2.0, wtfpl, ecl-2.0, epl-1.0, epl-2.0, etalab-2.0, eupl-1.1, agpl-3.0, gfdl, gpl, gpl-2.0, gpl-3.0, lgpl, lgpl-2.1, lgpl-3.0, isc, lppl-1.3c, ms-pl, mpl-2.0, odc-by, odbl, openrail++, osl-3.0, postgresql, ofl-1.1, ncsa, unlicense, zlib, pddl, lgpl-lr, deepfloyd-if-license, llama2, llama3, gemma, unknown, other, array]

licenses: - cc-by-2-0 multilinguality: - multilingual size_categories: - 10K<n<100K source_datasets: - original task_categories: - translation task_ids: [] paperswithcode_id: tatoeba pretty_name: Tatoeba

Dataset Card for Tatoeba

Dataset Summary

Tatoeba is a collection of sentences and translations. To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs. You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/Tatoeba.php E.g. dataset = load_dataset("tatoeba", lang1="en", lang2="he") The default date is v2021-07-22, but you can also change the date with dataset = load_dataset("tatoeba", lang1="en", lang2="he", date="v2020-11-09")

Supported Tasks and Leaderboards

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Languages

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Dataset Structure

Data Instances

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Data Fields

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Data Splits

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Dataset Creation

Curation Rationale

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Source Data

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Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

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Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

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Contributions

@loretoparisi

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