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Error code: FeaturesError Exception: ParserError Message: Error tokenizing data. C error: Expected 1 fields in line 8, saw 2 Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 231, 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 3335, 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 2096, in _head return next(iter(self.iter(batch_size=n))) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2296, in iter for key, example in iterator: File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1856, in __iter__ for key, pa_table in self._iter_arrow(): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1878, in _iter_arrow yield from self.ex_iterable._iter_arrow() File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 476, in _iter_arrow for key, pa_table in iterator: File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 323, in _iter_arrow for key, pa_table in self.generate_tables_fn(**gen_kwags): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/csv/csv.py", line 190, 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 8, saw 2
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Dataset origin: https://live.european-language-grid.eu/catalogue/corpus/9487
Mulve
Multi-Language Vocabulary Evaluation Data Set (MuLVE) is a data set consisting of vocabulary cards and real-life user answers, labeled whether the user answer is correct or incorrect. The data's source is user learning data from the Phase6 vocabulary trainer. The data set contains vocabulary questions in German and English, Spanish, and French as target language and is available in four different variations regarding pre-processing and deduplication.
It is split up into four tab-separated files, one for each variation, per train and test set. The files include the following columns:
cardId - numeric card ID
question - volcabulary card question
answer - volcabulary card answer
userAnswer - aragswer the user input
Label - turue if user answer is correct, False if not
language - tamrget language (English, French or Spanish)
The processed data set variations do not include the include \textbf{userAnswer} columns but the following additional columns:
question_norm - queestion normalized
answer_norm - aragswer normalized
userAnswer_norm - user answer normalized
Reference
@inproceedings{jacobsen-etal-2022-mulve,
title = "{M}u{LVE}, A Multi-Language Vocabulary Evaluation Data Set",
author = {Jacobsen, Anik and
Mohtaj, Salar and
M{\"o}ller, Sebastian},
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.70",
pages = "673--679",
abstract = "Vocabulary learning is vital to foreign language learning. Correct and adequate feedback is essential to successful and satisfying vocabulary training. However, many vocabulary and language evaluation systems perform on simple rules and do not account for real-life user learning data. This work introduces Multi-Language Vocabulary Evaluation Data Set (MuLVE), a data set consisting of vocabulary cards and real-life user answers, labeled indicating whether the user answer is correct or incorrect. The data source is user learning data from the Phase6 vocabulary trainer. The data set contains vocabulary questions in German and English, Spanish, and French as target language and is available in four different variations regarding pre-processing and deduplication. We experiment to fine-tune pre-trained BERT language models on the downstream task of vocabulary evaluation with the proposed MuLVE data set. The results provide outstanding results of {\textgreater} 95.5 accuracy and F2-score. The data set is available on the European Language Grid.",
}
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