Unsupervised Word-level Quality Estimation for Machine Translation Through the Lens of Annotators (Dis)agreement
Paper • 2505.23183 • Published • 1
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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: Couldn't cast array of type
struct<error_spans: list<item: struct<confidence: double, end: int64, label: string, start: int64>>, mt: string, sent_score: double, src: string>
to
{'attn_entropy_max': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'attn_entropy_mean': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_0': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_1': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_10': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_11': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_3': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_4': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_5': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_6': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_7': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_8': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_9': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_entropy_layer_0': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_entropy_layer_1': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_entropy_layer_10': Sequence(feature=Value(dt
...
gprob_layer_2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_3': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_4': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_5': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_6': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_7': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_8': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_9': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_variation': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_rank': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logprob': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logprobs_entropy': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'mcd_logprob_mean': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'mcd_logprob_var': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'mt': Value(dtype='string', id=None), 'mt_tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'src': Value(dtype='string', id=None)}
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 643, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2293, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2246, in cast_table_to_schema
arrays = [
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2247, in <listcomp>
cast_array_to_feature(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1796, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1796, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2109, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<error_spans: list<item: struct<confidence: double, end: int64, label: string, start: int64>>, mt: string, sent_score: double, src: string>
to
{'attn_entropy_max': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'attn_entropy_mean': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_0': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_1': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_10': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_11': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_3': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_4': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_5': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_6': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_7': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_8': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'blood_layer_9': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_entropy_layer_0': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_entropy_layer_1': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_entropy_layer_10': Sequence(feature=Value(dt
...
gprob_layer_2': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_3': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_4': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_5': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_6': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_7': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_8': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_layer_9': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_logprob_variation': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logit_lens_rank': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logprob': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'logprobs_entropy': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'mcd_logprob_mean': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'mcd_logprob_var': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None), 'mt': Value(dtype='string', id=None), 'mt_tokens': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'src': Value(dtype='string', id=None)}
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 1436, 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 1053, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 925, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1001, 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 1742, 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 1898, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
data dict |
|---|
{
"attn_entropy_max": [
2.339958190917968,
2.282905340194702,
2.343626737594604,
2.376308917999267,
2.450260162353515,
2.736898899078369,
2.5945565700531,
2.711625576019287,
2.669693946838379,
2.996870756149292,
3.097578763961792,
2.755706787109375,
2.77891230583190... |
{
"attn_entropy_max": [
2.225767135620117,
2.335447549819946,
2.434690475463867,
2.360906600952148,
2.709897756576538,
2.718684196472168,
2.739038228988647,
2.749853610992431,
2.786951541900634,
2.608992576599121,
2.805360794067383,
2.652894496917724,
2.966086864471... |
{
"attn_entropy_max": [
2.291632652282715,
2.344534158706665,
2.511959075927734,
2.501749992370605,
2.409031391143799,
2.654079437255859,
2.575302362442016,
2.5600898265838623,
2.74017333984375,
2.7494373321533203,
2.909408807754516,
3.219415187835693,
3.04526662826... |
{
"attn_entropy_max": [
2.31483793258667,
2.274999380111694,
2.354593276977539,
2.408150434494018,
2.442108869552612,
2.570751667022705,
2.509478569030761,
2.748116731643676,
2.712861537933349,
2.957579612731933,
2.994371652603149,
2.959711790084839,
2.9925956726074... |
{
"attn_entropy_max": [
2.174749612808227,
2.179407119750976,
2.3237521648406982,
2.377520084381103,
2.340901613235473,
2.702840805053711,
2.672287940979004,
2.832793951034546,
2.683857917785644,
2.9648597240448,
2.9893572330474854,
2.944262981414795,
2.974965572357... |
{"attn_entropy_max":[2.382016897201538,2.302021980285644,2.330931186676025,2.494935512542724,2.21885(...TRUNCATED) |
{"attn_entropy_max":[2.302583694458008,2.19846773147583,2.354688167572021,2.50736665725708,2.3666181(...TRUNCATED) |
{"attn_entropy_max":[2.388372182846069,2.358000755310058,2.361966133117676,2.461437225341797,2.66208(...TRUNCATED) |
{"attn_entropy_max":[2.703143119812011,2.6171882152557373,2.477118492126465,2.731252193450927,2.6596(...TRUNCATED) |
{"attn_entropy_max":[2.365798473358154,2.435630321502685,2.420302152633667,2.598120212554931,2.42446(...TRUNCATED) |
End of preview.
This repository contains the data for the paper Unsupervised Word-level Quality Estimation for Machine Translation Through the Lens of Annotators (Dis)agreement.
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