Datasets:
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Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
bigram: string
count: int64
prob: double
glyph_alphabet_size: int64
unigram_entropy_bits: double
redundancy: double
conditional_entropy_H2_given_H1_bits: double
glyph_tokens: int64
max_entropy_bits: double
bigram_entropy_bits: double
word_tokens: int64
hapax_fraction: double
word_types: int64
type_token_ratio: double
corpus: string
bigram_entropy_miller_madow_bits: double
hapax_legomena: int64
unigram_entropy_miller_madow_bits: double
to
{'corpus': Value('string'), 'glyph_tokens': Value('int64'), 'glyph_alphabet_size': Value('int64'), 'unigram_entropy_bits': Value('float64'), 'unigram_entropy_miller_madow_bits': Value('float64'), 'bigram_entropy_bits': Value('float64'), 'bigram_entropy_miller_madow_bits': Value('float64'), 'conditional_entropy_H2_given_H1_bits': Value('float64'), 'max_entropy_bits': Value('float64'), 'redundancy': Value('float64'), 'word_tokens': Value('int64'), 'word_types': Value('int64'), 'type_token_ratio': Value('float64'), 'hapax_legomena': Value('int64'), 'hapax_fraction': Value('float64')}
because column names don't match
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/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
bigram: string
count: int64
prob: double
glyph_alphabet_size: int64
unigram_entropy_bits: double
redundancy: double
conditional_entropy_H2_given_H1_bits: double
glyph_tokens: int64
max_entropy_bits: double
bigram_entropy_bits: double
word_tokens: int64
hapax_fraction: double
word_types: int64
type_token_ratio: double
corpus: string
bigram_entropy_miller_madow_bits: double
hapax_legomena: int64
unigram_entropy_miller_madow_bits: double
to
{'corpus': Value('string'), 'glyph_tokens': Value('int64'), 'glyph_alphabet_size': Value('int64'), 'unigram_entropy_bits': Value('float64'), 'unigram_entropy_miller_madow_bits': Value('float64'), 'bigram_entropy_bits': Value('float64'), 'bigram_entropy_miller_madow_bits': Value('float64'), 'conditional_entropy_H2_given_H1_bits': Value('float64'), 'max_entropy_bits': Value('float64'), 'redundancy': Value('float64'), 'word_tokens': Value('int64'), 'word_types': Value('int64'), 'type_token_ratio': Value('float64'), 'hapax_legomena': Value('int64'), 'hapax_fraction': Value('float64')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Voynich EVA Corpus Statistics
Deterministic, reproducible descriptive statistics for the text of the Voynich Manuscript in the EVA transcription (Takahashi, via the IVTFF LSI 2a interlinear file). Glyph-level and word-level frequency tables plus entropy estimates — including the Miller–Madow bias-corrected estimator.
This is a measurement artifact, not a decipherment claim. Every number is recomputable from the public transcription with the method described below.
Files
glyph_unigrams.jsonl— every EVA glyph with count and probability (23-symbol alphabet incl. word-space).glyph_bigrams.jsonl— the 200 most frequent glyph bigrams with count and probability.entropy_and_summary.json— corpus-level summary (below).
Headline numbers
| Quantity | Value |
|---|---|
| Glyph tokens | 190,954 |
| Glyph alphabet | 23 |
| Unigram entropy H₁ | 3.875 bits |
| Unigram entropy (Miller–Madow) | 3.875 bits |
| Bigram entropy H₂ | 6.010 bits |
| Conditional entropy H(X₂|X₁) | 2.135 bits |
| Max entropy log₂(23) | 4.524 bits |
| Redundancy | 0.143 |
| Word tokens / types | 30,946 / 7,156 |
| Type–token ratio | 0.231 |
| Hapax legomena | 5,057 (70.7% of types) |
The low second-order conditional entropy (~2.1 bits) is the well-documented Voynichese anomaly: successive glyphs are far more predictable than in natural language, one of the reasons the text resists standard cryptanalysis.
Method
Shannon entropy H = −Σ pᵢ log₂ pᵢ over the flattened Takahashi glyph stream.
Miller–Madow correction adds (K−1) / (2N ln 2) bits, with K observed symbols
and N tokens. Conditional entropy is H(X₂|X₁) = H₂ − H₁. Word statistics are
over whitespace-delimited EVA word tokens.
Provenance & license
Source transcription: EVA (Takahashi), René Zandbergen's IVTFF LSI file, a
community scholarly resource for the public-domain manuscript. These derived
statistics are released CC-BY-4.0. Companion decipherment substrate:
SMC17/zsym — Miller–Madow entropy, n-gram LMs,
and monoalphabetic/polyalphabetic/homophonic solvers with bootstrap CIs.
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