Lithuanian UPOS + morphology tagger (UD categories)
Token-classification tagger for Lithuanian: UPOS plus the morphological features that drive agreement (Case, Gender, Number, Tense, Person, Voice, Degree, VerbForm, Mood, Reflex), emitted as one combined label per word. Fine-tuned from EMBEDDIA/litlat-bert on MATAS v3.0 + UD_Lithuanian-ALKSNIS (~2.15M gold tokens).
Which model do I want?
| model | best for | emits |
|---|---|---|
| this one | highest UPOS/slots accuracy, UD categories | UPOS (incl. DET/AUX) + core FEATS |
| …-morphology-full | complete annotations | full UD FEATS + lemmas |
| …-morphology-vdu | Lithuanian accentuation pipelines | traditional-grammar categories |
Benchmarks (measured, full 684-sentence ALKSNIS gold test)
| metric | this model | UDPipe 2 reference |
|---|---|---|
| slots¹ | 89.1% | 89.2% |
| UPOS (same protocol) | 92.5% | 95.1% |
| CoNLL-18 official UPOS F1 (gold tokenization) | 94.0 | 95.2 |
| speed, tok/s (ONNX INT8 CPU vs network service) | 874 | 605 |
¹ slots = exact match of the agreement-feature projection (POS family + Case/Gender/Number/Tense/Person/Voice/Degree) that accentuation-style disambiguation consumes; DET/PRON and AUX/VERB merge inside the projection.
Not emitted by design (use the -full model instead): FEATS keys beyond the ten above (Definite, Polarity, PronType, …) and lemmas.
Usage (verified)
import torch
from transformers import AutoTokenizer, AutoModelForTokenClassification
repo = "alexbalandi/litlat-bert-lithuanian-morphology"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForTokenClassification.from_pretrained(repo)
words = ["Tas", "namas", "yra", "gražus"]
enc = tok(words, is_split_into_words=True, return_tensors="pt")
with torch.inference_mode():
logits = model(**enc).logits[0]
seen = set()
for pos, wid in enumerate(enc.word_ids(0)):
if wid is not None and wid not in seen:
seen.add(wid)
print(words[wid], model.config.id2label[int(logits[pos].argmax())])
# Tas DET|Case=Nom|Gender=Masc|Number=Sing
# namas NOUN|Case=Nom|Gender=Masc|Number=Sing
# ...
An onnx/ folder with an INT8 model (same logits output) is included
for CPU serving.
Training data & lineage
Gold corpora only, no NC-licensed model anywhere in the lineage:
MATAS v3.0
(CC BY 4.0; UD features reconstructed deterministically from its Jablonskis
XPOS) + UD_Lithuanian-ALKSNIS
(CC BY-SA 4.0), remaining contextual gaps filled by iterated
constrained-decoding self-training. Recipe and tooling:
github.com/alexbalandi/kirciuokle
(local/tagger-hf/, public domain).
License & attribution
CC BY-SA 4.0 (inherited from litlat-bert and ALKSNIS). Please credit: MATAS v3.0 (Rimkutė, Bielinskienė, Boizou, Dadurkevičius, Kovalevskaitė, Utka; CLARIN-LT), UD_Lithuanian-ALKSNIS (VDU), litlat-bert (Ulčar & Robnik-Šikonja, EMBEDDIA).