Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
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(/gutenberg/top_50/[]/[]) changed from string to number 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.
Vintage words
Word-frequency dictionaries built from ~16 billion words of English text written before 1900. For every word we counted how many times it appears across a large corpus of historical books, newspapers and pamphlets, and saved the result as simple {word: count} JSON files.
In short: a census of the pre-1900 English vocabulary.
What's in here
Seven per-source frequency dictionaries plus one combined dictionary:
| File | Source | Entries | Unique words | Tokens |
|---|---|---|---|---|
EEBO |
Early English Books Online | 34,935 | 2,415,448 | 698,964,004 |
ECCO |
Eighteenth Century Collections Online | 3,100 | 548,389 | 105,508,425 |
EVANS |
Evans Early American Imprints | 5,011 | 329,806 | 102,063,934 |
CLMET3.1 |
Corpus of Late Modern English Texts | 278 | 169,743 | 30,772,645 |
gutenberg |
Project Gutenberg (pre-1900 books) | 240,742 | 1,961,826 | 1,107,923,635 |
long-docs |
Long documents (books, BL/LoC/OTA) | 198,821 | 93,305,526 | 9,093,692,612 |
short-docs |
Short documents (newspapers, notices) | 13,077,552 | 76,154,718 | 5,025,802,850 |
ALL |
All of the above, merged | 13,560,439 | 131,934,489 | 16,164,728,105 |
summary.json lists the token totals, unique-word counts and top-50 words for each source.
How
- Sources. EEBO, ECCO, EVANS and CLMET3.1 are curated historical book collections are from vintage-v2 dataset; Gutenberg, long-docs and short-docs come from vintage-v1 dataset (newspapers, public-domain books and archives).
- Date filtering. Everything is confirmed pre-1900. The only collection containing later material (CLMET3.1, which runs up to ~2000) was filtered by its metadata
datefield, dropping 55 post-1900 books. - Tokenisation. Text was lowercased and split into words matching
[a-z]+(?:'[a-z]+)*- ASCII letters with internal apostrophes (don't,o'clock). Digits, hyphens, punctuation and non-Latin characters were excluded. - Counting. Simple occurrence counts per source, summed for the combined file.
A note on OCR and size
Much of this material is OCR'd from scans centuries old, so the long tail is full of scanning errors and broken words. The three largest dictionaries (words-ALL, words-long-docs, words-short-docs) are capped to their 10,000,000 most frequent words to keep the size small; beyond that rank the entries are overwhelmingly OCR noise.
The unique_words figures in the table above are the true pre-cap totals.
Why bother
A frequency-ranked snapshot of what English actually looked like before 1900 is useful for:
- Anachronism detection -- deciding whether a word plausibly existed in historical text (a modern term like
internetorchemotherapysimply never appears here). - Building or evaluating historical language models and period-accurate text generation.
- Filtering/curating pre-1900 corpora and spotting modern contamination.
- Lexicography and linguistics -- spelling variation, vocabulary shift, and word-frequency studies over time.
Format
Each file is a JSON object mapping a word to its integer count, sorted by descending frequency:
{
"the": 1086964914,
"of": 635302823,
"and": 496624990
}
Citation
@misc{vintage-words,
title = {Vintage-words},
author = {Cristi Constantin},
year = {2026},
url = {https://huggingface.co/datasets/croqaz/vintage-words}
}
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