The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 101, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
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/split_names.py", line 66, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
hyper-glyphy — trained artifacts mirror
Companion artifact store for github.com/ebrinz/hyper-glyphy: cross-lingual word-embedding alignment for six ancient languages (Sumerian, Akkadian, Hittite, Ancient Greek, Egyptian, Sanskrit) into GloVe 300d and whitened-EmbeddingGemma 768d English spaces.
Everything here is computed output of the pipelines in the GitHub repo, mirrored so results can be reproduced exactly without retraining (FastText training is non-deterministic, so retrained vectors differ slightly from the published numbers). Raw third-party corpora are NOT included — each slot's README in the GitHub repo documents the fetch steps (DCS, Diorisis, ORACC, ETCSL, TLHdig, Cologne CDSL, etc.).
Layout
Mirrors the GitHub repo's gitignored paths:
shared/models/ English caches: EmbeddingGemma 768d (gloss/bare,
raw + whitened) and whitening transforms
languages/<slot>/models/ FastText model + .vec, fused 1536d npz,
Ridge weights, Procrustes maps
languages/<slot>/data/processed/ merged/cleaned corpora (per-text doc IDs),
english_anchors.json, anchor stats
languages/<slot>/data/dictionaries/ derived gloss lexica (LSJ, Monier-Williams)
languages/<slot>/results/ alignment results + eval-suite artifacts
languages/<slot>/final_output/ production aligned vectors + vocab
Note: in akkadian/greek/hittite/sumerian, FastText files carry the legacy
name fasttext_sumerian.* (cloned script kept the output filename); Sanskrit
and Egyptian use their own slot names.
Reproducing
git clone https://github.com/ebrinz/hyper-glyphy
cd hyper-glyphy
hf download ebrinz/hyper-glyphy-artifacts --repo-type dataset --local-dir .
# then e.g.:
python languages/sanskrit/scripts/09b_align_gemma.py --mode whitened
python shared/scripts/procrustes_align.py --slot sanskrit
The English GloVe cache (glove.6B.300d.txt, Stanford NLP, PDDL) ships under
languages/sumerian/data/processed/; other slots reference it — recreate the
symlinks or copy it (see each slot README).
Licensing (mixed — per source)
- Trained vectors, weights, maps, results (our computation): CC BY 4.0.
- Sanskrit-derived files (
languages/sanskrit/data/dictionaries/mw_glosses.json,english_anchors.jsonand downstream anchors): derived from the Cologne CDSL digitization of Monier-Williams (1899), CC BY-NC-SA 3.0 — non-commercial, share-alike, attribution: The Sanskrit Library / Thomas Malten / Universität zu Köln. Sanskrit corpora derive from the Digital Corpus of Sanskrit (Oliver Hellwig), CC BY 4.0. - Greek-derived gloss files: from Perseus LSJ (CC BY-SA 4.0, Perseus Digital Library).
- Other slots' processed corpora: derived from ORACC / ETCSL / TLHdig / Diorisis / TLA — see the GitHub repo's per-slot READMEs for source attributions.
glove.6B.300d.txt: Stanford GloVe, Public Domain Dedication and License v1.0.
If you use these artifacts, cite the underlying sources per slot (see GitHub READMEs).
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