Both registries already have a `catena`: an HPE blockchain client on PyPI,
and a JavaScript OOP wrapper at 2.2.0 on npm. Neither is going anywhere, so
the distributions take a name that is free on both.
The Python import is unaffected — a distribution name and a module name are
independent there, so `pip install catena-corpus` still gives `import
catena`, and nothing in the examples or the bindings changes. On npm the
package name is the import specifier, so that one does change: `import
{ chunkMarkdown } from "catena-corpus"`.
The crates keep their own names. `catena` is free on crates.io.
book-pipeline had its own copy of bibclean's author recovery. Bound on all
four surfaces so it can stop.
Checked against that Python over all 5,385 books in the library: 5,375
identical. On the other ten the Python had taken a translator, an illustrator
or the publisher off a copyright line — Sally-Ann Spencer for *The Dwarves*,
Thomas Nelson for a table of contents, C. S. Lewis for a book about Tolkien —
and bibclean either finds the real author in the Library of Congress block or
says nothing. Every one of the ten was read by hand.
Cuts a reference list into entries — at the note numbers where there are any,
at the layout where there are not — and reads each as far as its punctuation
allows. Fields that would not parse are named in `unsettled` rather than
guessed at, and `--prompt` asks a model about exactly those and no others.
Over 202 real reference sections from the corpus: 8,566 references, 95% with
a title, 82% with a year, 46% settling every field with no model involved.
A title is refused where the entry was never divided, so no record reports
its own text back as the work it cites.
`ibid.` is flagged and left alone — it points at a work, and the entry does
not say which. A rule of dashes is filled in, because the style defines it as
the author above.
chunkmd gains `note_starts` and `cites_something`: recognising a page of
notes and splitting one are the same knowledge, and two copies of that
pattern would drift.
The hashed bag of words asks only whether two stretches repeat the same
words, and cannot tell that ships and vessels are one subject. An embedding
can. segment_with takes one vector per window and falls back to the words
when it is given none, or the wrong number of them — a caller that embedded
some other recording's windows is asking about that recording.
windows_of gives the text to embed, in the order the vectors come back in,
so nobody has to reimplement the windowing pass to use this.
Bound in all three languages, and on the command line as --windows and
--embeddings, which pipe through emb. The C ABI version goes to 2:
catena_segment_cues gained an argument.
funkpower