Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: ValueError
Message: Illegal slicing argument for scalar dataspace
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 87, in _generate_tables
pa_table = _recursive_load_arrays(h5, self.info.features, start, end)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 273, in _recursive_load_arrays
arr = _recursive_load_arrays(dset, features[path], start, end)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 273, in _recursive_load_arrays
arr = _recursive_load_arrays(dset, features[path], start, end)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 275, in _recursive_load_arrays
arr = _load_array(dset, path, start, end)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 242, in _load_array
arr = dset[start:end]
~~~~^^^^^^^^^^^
File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
File "/usr/local/lib/python3.14/site-packages/h5py/_hl/dataset.py", line 931, in __getitem__
selection = sel2.select_read(fspace, args)
File "/usr/local/lib/python3.14/site-packages/h5py/_hl/selections2.py", line 101, in select_read
return ScalarReadSelection(fspace, args)
File "/usr/local/lib/python3.14/site-packages/h5py/_hl/selections2.py", line 86, in __init__
raise ValueError("Illegal slicing argument for scalar dataspace")
ValueError: Illegal slicing argument for scalar dataspaceNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
review-sentiment — derived assets
Trained model weight files for a non-commercial student machine-learning coursework project (Korean movie-review sentiment classification: TF-IDF+LogisticRegression, LSTM, KLUE-BERT). This repo exists only so the project's Streamlit demo app can fetch these weights at runtime without hitting GitHub's Git LFS storage/bandwidth quota.
Source & license
- Training data: NSMC (Naver Sentiment Movie Corpus)
— 200,000 Korean movie reviews, positive/negative labels. Not hosted in this repo;
the main repo's
src/data/load_nsmc.pydownloads it directly from the canonicale9t/nsmcsource on first use. - KLUE-BERT base checkpoint: fine-tuned from
klue/bert-base(CC-BY-SA-4.0). - LSTM / TF-IDF+LogisticRegression: trained from scratch on NSMC by this project; no third-party model weights involved.
This mirror hosts only the resulting trained weight files for the coursework's own demo use case — it is not an independent redistribution channel for NSMC itself.
Contents
models/klue_bert/model.safetensors— fine-tuned KLUE-BERT sequence-classification weightsmodels/lstm/model.h5— trained LSTM weightsmodels/tfidf_lr/model.pkl— trained TF-IDF + LogisticRegression classifiermodels/tfidf_lr/vectorizer.pkl— fitted TF-IDF vectorizer
Note: this mirror only hosts the model weight binaries the Streamlit app needs at
runtime (_resolve() in app.py). Tokenizer configs, metrics, training scripts, and
the project's own code live in the main repo (Ketose333/review-sentiment), not here.
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