Cicero-Similis

Model description

A Latin Language Model, trained on Latin texts, and evaluated using the corpus of Cicero, as described in the paper What Would Cicero Write? -- Examining Critical Textual Decisions with a Language Model by Todd Cook, Published in Ciceroniana On Line, Vol. V, #2.

Intended uses & limitations

How to use

Normalize text using JV Replacement and tokenize using CLTK to separate enclitics such as "-que", then:

from transformers import BertForMaskedLM, AutoTokenizer, FillMaskPipeline
tokenizer = AutoTokenizer.from_pretrained("cook/cicero-similis")
model = BertForMaskedLM.from_pretrained("cook/cicero-similis")
fill_mask = FillMaskPipeline(model=model, tokenizer=tokenizer, top_k=10_000)
# Cicero, De Re Publica, VI, 32, 2
# "animal" is found in A, Q, PhD manuscripts
# 'anima' H^1 Macr. et codd. Tusc.
results = fill_mask("inanimum est enim omne quod pulsu agitatur externo; quod autem est [MASK],")

Limitations and bias

Currently the model training data excludes modern and 19th century texts, but that weakness is the model's strength; it's not aimed to be a one-size-fits-all model.

Training data

Trained on the corpora Phi5, Tesserae, Thomas Aquinas, and Patrologes Latina.

Training procedure

5 epochs, masked language modeling .15, effective batch size 32

Eval results

A novel evaluation metric is proposed in the paper What Would Cicero Write? -- Examining Critical Textual Decisions with a Language Model by Todd Cook, Published in Ciceroniana On Line, Vol. V, #2.

BibTeX entry and citation info

TODO What Would Cicero Write? -- Examining Critical Textual Decisions with a Language Model by Todd Cook, Published in Ciceroniana On Line, Vol. V, #2.