SCTA Doc2Vec (2026.10)
Doc2Vec document vectors for the texts of the Scholastic Commentaries and Texts Archive, trained on scta/scta-corpus 2026.10, built with scta-scikit. Similar vectors mean similar content, even in different words. 2.1 GB.
| model | |
|---|---|
semantic |
trained on the paragraph texts: a vector for each unit of every text's structure, down to paragraphs |
semantic_lem |
the same, trained on the lemmatized texts |
semantic_wo_paras |
semantic without the paragraph vectors (levels and items only; smaller) |
Each is models/<model>/vectors.npy (300 dimensions, float32), labels.json
(the SCTA resource id of each row) and info.json; no pickles.
Training: PV-DM, window 10, min_count 5, negative 5, 40 epochs, seed 42.
Use
import scta_scikit as sc
sc.download(doc2vec=True) # these models and the corpus they were trained on
sc.download(doc2vec=["semantic_lem"]) # one of them
Then scta sim <resource id> --semantic.
Versions
A release is tagged with the corpus version it was trained on (2026.10;
2026.10-2 for a retraining on the same corpus), and manifest.json names
that corpus. Models are released less often than the corpus: a newer corpus
has texts these vectors don't cover.
License
Derived from the SCTA corpus and published under the same license, Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0), https://creativecommons.org/licenses/by-nc-sa/4.0/.
Built 2026-10-24T12:44:01Z with scta-scikit 553c3a9; see manifest.json.