Downloads · 30 days
0
ancatmara/early-modern-irish-ft-vectors
early-modern-irish-ft-vectors is a feature extraction model from ancatmara. Use it when you need embeddings to search or compare text. It is set up for gensim. The card lists the license as cc-by-nc-sa-4.0.
Early Modern Irish FastText models were trained on Early Irish texts from CELT and the book subcorpus of Historical Irish Corpus. A text was included in the training dataset if "Early Modern Irish", "Classical Modern…
Downloads · 30 days
0
Access
Public
Updated Aug 15, 2024
Repo size
449 MB
Likes
0
Public
Click a slice to open those files.
.txt228 MB · 100%
From the Hugging Face model README
Early Modern Irish FastText models were trained on Early Irish texts from CELT and the book subcorpus of Historical Irish Corpus. A text was included in the training dataset if "Early Modern Irish", "Classical Modern Irish" or the dates "1200-1700" were explicitely mentioned in its metadata on CELT, including texts marked as "Old, Middle and Early Modern Irish". Therefore, Early Modern Irish models can have some Old and Middle Irish words in the vocabulary, as well as some Latin due to code-switching. One can argue that 1700 is a bit too late to be the end date for Early Modern Irish, and that we should speak of Modern Irish starting from the 1500s, but we prefer to rely on language labels assigned to our source texts by editors, and to stretch out a bit for better data coverage.
There are 3 models in this familily:
early_modern_irish_cased_ft_100_5_2.txtearly_modern_irish_lower_ft_100_5_2.txtearly_modern_irish_lower_demutated_ft_100_5_2.txtAll models are trained with the same hyperparameters (emb_size=100, window=5, min_count=2, n_epochs=100) and saved as KeyedVectors (see Gensim Documentation).
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(repo_id="ancatmara/early-modern-irish-ft-vectors", filename="early_modern_irish_cased_ft_100_5_2.txt")
model = KeyedVectors.load_word2vec_format(model_path, binary=False)
model.similar_by_word('Laighen')
Out:
>>> [('Laighe', 0.8656445741653442),
('Laighenáin', 0.8017030358314514),
('Osraighe', 0.7939673662185669),
('Laighean', 0.7764200568199158),
('Connacht', 0.757500171661377),
('Mumhan', 0.749495267868042),
('Ereann', 0.7490534782409668),
('Laigen', 0.7099151611328125),
('Maighen', 0.704881489276886),
('Laighnibh', 0.7041199207305908)]