oeg/software_benchmark_multidomain
Token Classification
•
Updated
•
18
Error code: DatasetGenerationError Exception: CastError Message: Couldn't cast tokens: list<item: string> child 0, item: string labels: list<item: int64> child 0, item: int64 -- schema metadata -- huggingface: '{"info": {"features": {"tokens": {"feature": {"dtype": "str' + 130 to {'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'ner_tags': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'input_ids': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None), 'token_type_ids': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None), 'attention_mask': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None), 'labels': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)} because column names don't match Traceback: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1995, in _prepare_split_single for _, table in generator: File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/arrow/arrow.py", line 71, in _generate_tables yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/arrow/arrow.py", line 59, in _cast_table pa_table = table_cast(pa_table, self.info.features.arrow_schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast return cast_table_to_schema(table, schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema raise CastError( datasets.table.CastError: Couldn't cast tokens: list<item: string> child 0, item: string labels: list<item: int64> child 0, item: int64 -- schema metadata -- huggingface: '{"info": {"features": {"tokens": {"feature": {"dtype": "str' + 130 to {'tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'ner_tags': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), 'input_ids': Sequence(feature=Value(dtype='int32', id=None), length=-1, id=None), 'token_type_ids': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None), 'attention_mask': Sequence(feature=Value(dtype='int8', id=None), length=-1, id=None), 'labels': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)} because column names don't match The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1529, in compute_config_parquet_and_info_response parquet_operations = convert_to_parquet(builder) File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1154, in convert_to_parquet builder.download_and_prepare( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare self._download_and_prepare( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare self._prepare_split(split_generator, **prepare_split_kwargs) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
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tokens
sequence | ner_tags
sequence | input_ids
sequence | token_type_ids
sequence | attention_mask
sequence | labels
sequence |
---|---|---|---|---|---|
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[
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[
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The corpus have been built using two corpora in software mentions.
To build this corpus, we have removed the annotations of other entities such as version, url and those which are related with the relation of teh entity with the text. IN this case, we only use the label Application_Mention.
To reconciliate both corpora, we have mapping the labels of both corpora. Also, some decisions about the annotations have been taken, for example, in the case of Microsoft Excel, we have decided to annotate Excel as software mention, not the whole text.