| --- |
| license: cc-by-nc-sa-4.0 |
| language: |
| - la |
| tags: |
| - latin |
| - medieval |
| - doc2vec |
| - embeddings |
| datasets: |
| - scta/scta-corpus |
| --- |
| |
| # SCTA Doc2Vec (2026.10) |
|
|
| Doc2Vec document vectors for the texts of the [Scholastic Commentaries and Texts Archive](https://scta.info), |
| **trained on [scta/scta-corpus 2026.10](https://huggingface.co/datasets/scta/scta-corpus/tree/2026.10)**, built with [scta-scikit](https://pypi.org/project/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 |
| |
| ```python |
| 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`. |
| |
