Lt Core News Lg
CPU-optimized Lithuanian processing pipeline with complete NLP functions including tokenization, POS tagging, dependency parsing, and named entity recognition
Downloads 52
Release Time : 3/2/2022
Model Overview
Large Lithuanian model trained on the UD Lithuanian ALKSNIS corpus, supporting full natural language processing workflows including POS tagging, dependency parsing, and named entity recognition
Model Features
Complete NLP Pipeline
Provides a full processing pipeline from tokenization to named entity recognition, including 8 processing components
CPU Optimization
Specifically optimized for CPU usage scenarios, suitable for resource-constrained environments
High-quality Word Vectors
Includes 500,000 pretrained word vectors covering a wide vocabulary
Multitask Support
Simultaneously supports multiple NLP tasks such as POS tagging, dependency parsing, and named entity recognition
Model Capabilities
Tokenization
POS Tagging
Dependency Parsing
Named Entity Recognition
Lemmatization
Morphological Analysis
Sentence Boundary Detection
Use Cases
Text Analysis
Lithuanian Text Processing
Processing and analyzing Lithuanian text content
Accurately identifies parts of speech, syntactic relationships, and named entities
Information Extraction
Entity Recognition
Extracting entity information such as person names and locations from Lithuanian text
Achieves an F1 score of 0.79
🚀 lt_core_news_lg
This is a Lithuanian pipeline optimized for CPU, which can handle various natural language processing tasks such as named - entity recognition, part - of - speech tagging, and more.
📚 Documentation
Details: https://spacy.io/models/lt#lt_core_news_lg
The Lithuanian pipeline is optimized for CPU. Components include: tok2vec, morphologizer, tagger, parser, lemmatizer (trainable_lemmatizer), senter, ner.
Property | Details |
---|---|
Name | lt_core_news_lg |
Version | 3.7.0 |
spaCy | >=3.7.0,<3.8.0 |
Default Pipeline | tok2vec , morphologizer , tagger , parser , lemmatizer , attribute_ruler , ner |
Components | tok2vec , morphologizer , tagger , parser , lemmatizer , senter , attribute_ruler , ner |
Vectors | 500000 keys, 500000 unique vectors (300 dimensions) |
Sources | UD Lithuanian ALKSNIS v2.8 (Utka, Andrius; Rimkutė, Erika; Bielinskienė, Agnė; Kovalevskaitė, Jolanta; Boizou, Loïc; Aleksandravičiūtė, Gabrielė; Brokaitė, Kristina; Zeman, Daniel; Perkova, Natalia; Griciūtė, Bernadeta) TokenMill NER Corpus (TokenMill) Explosion fastText Vectors (cbow, OSCAR Common Crawl + Wikipedia) (Explosion) |
License | CC BY - SA 4.0 |
Author | Explosion |
Model Performance
The model has been evaluated on several token - classification tasks, and the following are the performance metrics:
Task | Metric | Value |
---|---|---|
NER | NER Precision | 0.7492163009 |
NER | NER Recall | 0.8369184592 |
NER | NER F Score | 0.7906427221 |
TAG | TAG (XPOS) Accuracy | 0.8803910542 |
POS | POS (UPOS) Accuracy | 0.9519401306 |
MORPH | Morph (UFeats) Accuracy | 0.8889081156 |
LEMMA | Lemma Accuracy | 0.8613069238 |
UNLABELED_DEPENDENCIES | Unlabeled Attachment Score (UAS) | 0.7444831591 |
LABELED_DEPENDENCIES | Labeled Attachment Score (LAS) | 0.6760280843 |
SENTS | Sentences F - Score | 0.8437246964 |
Label Scheme
View label scheme (1669 labels for 4 components)
Component | Labels |
---|---|
morphologizer |
Definite=Ind|Gender=Neut|POS=VERB|Polarity=Pos|Tense=Pres|VerbForm=Part|Voice=Pass , POS=VERB|Polarity=Pos|VerbForm=Inf , Case=Gen|Definite=Def|Degree=Pos|Gender=Fem|Number=Plur|POS=ADJ , Case=Gen|Gender=Fem|Number=Plur|POS=NOUN , Case=Gen|Gender=Masc|Number=Plur|POS=NOUN , Case=Acc|Gender=Masc|Number=Plur|POS=NOUN , POS=VERB|Polarity=Pos|Tense=Pres|VerbForm=Ger , Case=Gen|Gender=Masc|Number=Sing|POS=NOUN , POS=CCONJ , POS=PUNCT , Case=Gen|Definite=Ind|Gender=Masc|Number=Plur|POS=PRON|PronType=Ind , Case=Acc|Definite=Ind|Degree=Pos|Gender=Fem|Number=Plur|POS=ADJ , Case=Acc|Gender=Fem|Number=Plur|POS=NOUN , Case=Loc|Gender=Fem|Number=Plur|POS=NOUN , Case=Gen|Definite=Ind|Gender=Masc|Number=Plur|POS=VERB|Polarity=Pos|Tense=Pres|VerbForm=Part|Voice=Act , Case=Acc|Gender=Masc|Number=Sing|POS=NOUN , Case=Acc|Definite=Ind|Gender=Fem|Number=Plur|POS=PRON|PronType=Ind , Case=Acc|Definite=Ind|Gender=Fem|Number=Sing|POS=DET|PronType=Dem , Case=Acc|Gender=Fem|Number=Sing|POS=NOUN , Aspect=Perf|Mood=Ind|Number=Sing|POS=VERB|Person=3|Polarity=Pos|Tense=Past|VerbForm=Fin , Aspect=Perf|Case=Nom|Definite=Ind|Gender=Masc|Number=Sing|POS=VERB|Polarity=Pos|Tense=Past|VerbForm=Part|Voice=Act , Case=Gen|Gender=Fem|Number=Sing|POS=NOUN , Case=Nom|Gender=Masc|Number=Sing|POS=NOUN , Abbr=Yes|POS=X , AdpType=Prep|Case=Gen|POS=ADP , Case=Gen|Gender=Masc|Number=Sing|POS=PROPN , Case=Nom|Definite=Ind|Degree=Pos|Gender=Fem|Number=Plur|POS=ADJ , Case=Nom|Gender=Fem|Number=Plur|POS=NOUN , Case=Ins|Gender=Masc|Number=Sing|POS=NOUN , Mood=Ind|Number=Plur|POS=VERB|Person=3|Polarity=Pos|Tense=Pres|VerbForm=Fin , Case=Acc|Definite=Ind|Degree=Pos|Gender=Masc|Number=Sing|POS=ADJ , Mood=Cnd|POS=VERB|Person=3|Polarity=Pos|VerbForm=Fin , Case=Gen|Definite=Ind|Gender=Fem|Number=Plur|POS=PRON|PronType=Ind , Case=Nom|Gender=Masc|Number=Plur|POS=NOUN , Mood=Ind|Number=Plur|POS=AUX|Person=3|Polarity=Pos|Tense=Pres|VerbForm=Fin , Degree=Pos|POS=ADV , Case=Nom|Definite=Ind|Degree=Pos|Gender=Masc|Number=Plur|POS=ADJ , Degree=Pos|Hyph=Yes|POS=ADV , Hyph=Yes|POS=X , Case=Nom|Definite=Ind|Gender=Masc|Number=Plur|POS=PRON|Person=3|PronType=Prs , POS=SCONJ , Mood=Ind|POS=VERB|Person=3|Polarity=Pos|Tense=Pres|VerbForm=Fin , Case=Nom|Definite=Ind|POS=PRON|PronType=Ind , Case=Nom|Definite=Ind|Gender=Fem|Number=Sing|POS=DET|PronType=Dem , Case=Nom|Gender=Fem|Number=Sing|POS=NOUN , Mood=Ind|Number=Sing|POS=VERB|Person=3|Polarity=Pos|Reflex=Yes|Tense=Pres|VerbForm=Fin , Mood=Ind|Number=Sing|POS=VERB|Person=3|Polarity=Pos|Tense=Pres|VerbForm=Fin , Case=Acc|Definite=Ind|POS=PRON|PronType=Ind , POS=PART , Case=Gen|Definite=Ind|Degree=Pos|Gender=Fem|Number=Sing|POS=ADJ , Mood=Ind|Number=Plur|POS=VERB|Person=3|Polarity=Pos|Reflex=Yes|Tense=Pres|VerbForm=Fin , Case=Gen|Definite=Ind|Gender=Masc|Number=Plur|POS=DET|PronType=Dem , Case=Ins|Gender=Masc|NumForm=Word|NumType=Card|POS=NUM , Case=Ins|Gender=Masc|Number=Plur|POS=NOUN , Case=Ins|Definite=Ind|Degree=Pos|Gender=Masc|Number=Sing|POS=ADJ , Definite=Ind|Gender=Neut|POS=DET|PronType=Dem , Mood=Ind|POS=AUX|Person=3|Polarity=Pos|Tense=Pres|VerbForm=Fin , Definite=Ind|Degree=Pos|Gender=Neut|POS=ADJ , Case=Nom|Definite=Ind|Gender=Masc|Number=Sing|POS=PRON|PronType=Ind , Case=Nom|Gender=Masc|Number=Sing|POS=PROPN , Case=Loc|Definite=Ind|Gender=Fem|Number=Sing|POS=VERB|Polarity=Pos|Tense=Pres|VerbForm=Part|Voice=Pass , Case=Gen|Definite=Def|Degree=Pos|Gender=Masc|Number=Sing|POS=ADJ , Case=Loc|Gender=Fem|Number=Sing|POS=NOUN , Aspect=Perf|POS=VERB|Polarity=Pos|Reflex=Yes|Tense=Past|VerbForm=Ger , Case=Dat|Gender=Masc|Number=Sing|POS=NOUN , Mood=Ind|Number=Plur|POS=VERB|Person=3|Polarity=Pos|Tense=Fut|VerbForm=Fin , Mood=Ind|POS=VERB|Person=3|Polarity=Pos|Tense=Fut|VerbForm=Fin , POS=VERB|Polarity=Pos|Reflex=Yes|VerbForm=Inf , Degree=Cmp|POS=ADV , Case=Gen|Gender=Fem|Number=Sing|POS=PROPN , Mood=Ind|Number=Sing|POS=VERB|Person=3|Polarity=Pos|Tense=Fut|VerbForm=Fin , Case=Gen|Definite=Ind|Gender=Masc|Number=Sing|POS=DET|PronType=Dem , Mood=Ind|POS=VERB|Person=3|Polarity=Neg|Tense=Pres|VerbForm=Fin , Mood=Ind|Number=Sing|POS=VERB|Person=3|Polarity=Neg|Tense=Pres|VerbForm=Fin , Case=Gen|Definite=Ind|Gender=Masc|Number=Sing|POS=PRON|PronType=Ind , Definite=Ind|NumForm=Digit|POS=NUM , Case=Gen|Gender=Masc|NumForm=Word|NumType=Card|Number=Plur|POS=NUM , Case=Nom|Definite=Ind|Degree=Pos|Gender=Masc|Number=Sing|POS=ADJ , Case=Acc|Gender=Masc|NumForm=Word|NumType=Card|Number=Sing|POS=NUM , Case=Dat|Definite=Ind|Number=Sing|POS=PRON|Person=1|PronType=Prs , Case=Gen|Definite=Ind|Gender=Masc|Number=Plur|POS=DET|PronType=Tot , Case=Nom|Definite=Ind|Gender=Masc|Number=Plur|POS=VERB|Polarity=Pos|Tense=Past|VerbForm=Part|Voice=Pass , Case=Loc|Definite=Ind|Gender=Fem|Number=Plur|POS=PRON|PronType=Ind , Case=Nom|Gender=Masc|NumForm=Word|NumType=Card|Number=Plur|POS=NUM , NumForm=Word|NumType=Card|POS=NUM , Case=Nom|Definite=Ind|Gender=Fem|Hyph=Yes|Number=Plur|POS=DET|PronType=Dem , Mood=Ind|Number=Sing|POS=VERB|Person=1|Polarity=Pos|Tense=Pres|VerbForm=Fin , Case=Nom|Definite=Def|Degree=Pos|Gender=Masc|Number=Sing|POS=ADJ , Case=Nom|Definite=Ind|Gender=Masc|Number=Sing|POS=DET|PronType=Int,Rel , Case=Acc|Definite=Ind|Gender=Masc|Number=Sing|POS=DET|PronType=Dem , Case=Dat|Gender=Masc|Number=Plur|POS=NOUN , Case=Nom|Gender=Fem|Number=Sing|POS=PROPN , Case=Nom|Definite=Ind|Gender=Fem|Number=Sing|POS=PRON|Person=3|PronType=Prs , Hyph=Yes|POS=PART , Mood=Cnd|Number=Sing|POS=AUX|Person=3|Polarity=Pos|VerbForm=Fin , Case=Nom|Definite=Ind|Gender=Masc|Number=Sing|POS=PRON|Person=3|PronType=Prs , Case=Loc|Gender=Masc|Number=Sing|POS=NOUN , AdpType=Prep|Case=Acc|POS=ADP , Mood=Cnd|Number=Sing|POS=VERB|Person=3|Polarity=Pos|VerbForm=Fin , Case=Gen|Definite=Def|Gender=Fem|NumForm=Combi|NumType=Ord|Number=Sing|POS=NUM , Case=Nom|Definite=Def|Gender=Fem|NumForm=Word|NumType=Ord|Number=Sing|POS=NUM , Aspect=Perf|Mood=Ind|Number=Plur|POS=VERB|Person=3|Polarity=Pos|Tense=Past|VerbForm=Fin , Case=Acc|Definite=Ind|Degree=Pos|Gender=Fem|Number=Sing|POS=ADJ , Mood=Ind|Number=Plur|POS=VERB|Person=3|Polarity=Neg|Tense=Pres|VerbForm=Fin , Definite=Ind|NumForm=Roman|POS=NUM , Case=Gen|Definite=Ind|Gender=Masc|Number=Plur|POS=PRON|Person=3|PronType=Prs , Case=Gen|Definite=Ind|Degree=Pos|Gender=Masc|Number=Plur|POS=ADJ , Aspect=Perf|Case=Nom|Definite=Ind|Gender=Masc|Number=Sing|POS=VERB|Polarity=Pos|Reflex=Yes|Tense=Past|VerbForm=Part|Voice=Act , Case=Gen|Definite=Ind|Gender=Fem|NumForm=Word|NumType=Ord|Number=Sing|POS=NUM , Case=Nom|Definite=Ind|Gender=Masc|Number=Sing|POS=DET|PronType=Dem , Case=Nom|Definite=Ind|Gender=Masc|Mood=Nec|Number=Sing|POS=VERB|Polarity=Pos|VerbForm=Part , Case=Nom|Definite=Ind|Degree=Cmp|Gender=Fem|Number=Plur|POS=ADJ , Aspect=Perf|Case=Acc|Definite=Ind|Gender=Masc|Number=Plur|POS=VERB|Polarity=Pos|Reflex=Yes|Tense=Past|VerbForm=Part|Voice=Act , Case=Dat|Definite=Ind|Gender=Masc|Number=Plur|POS=PRON|Person=3|PronType=Prs , Aspect=Perf|Mood=Ind|POS=VERB|Person=3|Polarity=Pos|Tense=Past|VerbForm=Fin , Aspect=Perf|Case=Nom|Definite=Ind|Gender=Masc|Number=Plur|POS=VERB|Polarity=Pos|Tense=Past|VerbForm=Part|Voice=Act , Case=Gen|Definite=Ind|Degree=Pos|Gender=Fem|Number=Plur|POS=ADJ , Case=Nom|Definite=Ind|Gender=Fem|Number=Plur|POS=DET|PronType=Int,Rel , Degree=Sup|POS=ADV , Case=Nom|Definite=Ind|Gender=Fem|Number=Plur|POS=VERB|Polarity=Pos|Tense=Past|VerbForm=Part|Voice=Pass , Case=Gen|Definite=Ind|Gender=Fem|Mood=Nec|Number=Plur|POS=VERB|Polarity=Neg|VerbForm=Part , Mood=Ind|Number=Sing|POS=AUX|Person=3|Polarity=Pos|Tense=Pres|VerbForm=Fin , Case=Ins|Definite=Ind|Gender=Masc|Number=Sing|POS=DET|PronType=Int,Rel , Case=Acc|Definite=Ind|Gender=Masc|Number=Sing|POS=VERB|Polarity=Pos|Tense=Pres|VerbForm=Part|Voice=Pass , Case=Acc|Gender=Fem|NumForm=Word|NumType=Card|Number=Sing|POS=NUM , Mood=Ind|Number=Sing|POS=VERB|Person=1|Polarity=Pos|Reflex=Yes|Tense=Pres|VerbForm=Fin , Mood=Ind|Number=Plur|POS=VERB|Person=3|Polarity=Neg|Tense=Fut|VerbForm=Fin , Case=Gen|Definite=Def|Gender=Masc|NumForm=Combi|NumType=Ord|Number=Sing|POS=NUM , Case=Nom|Definite=Ind|Gender=Masc|Number=Sing|POS=VERB|Polarity=Pos|Tense=Pres|VerbForm=Part|Voice=Pass , AdpType=Prep|Case=Ins|POS=ADP , Degree=Pos|POS=ADV|PronType=Int,Rel , Case=Gen|Definite=Ind|Gender=Masc|POS=PRON|PronType=Ind , Case=Nom|Definite=Ind|Gender=Masc|Number=Sing|POS=VERB|Polarity=Pos|Tense=Past|VerbForm=Part|Voice=Pass , Case=Gen|Gender=Fem|NumForm=Word|NumType=Card|POS=NUM , Case=Gen|Gender=Masc|Number=Sing|POS=NOUN|Reflex=Yes , Case=Ins|Definite=Ind|Degree=Pos|Gender=Fem|Number=Plur|POS=ADJ , Case=Ins|Gender=Fem|Number=Plur|POS=NOUN , Definite=Ind|Gender=Neut|POS=VERB|Polarity=Neg|Tense=Past|VerbForm=Part|Voice=Pass , Case=Dat|Gender=Fem|Number=Sing|POS=NOUN , Case=Ins|Gender=Fem|Number=Sing|POS=PROPN , Case=Nom|Definite=Ind|Gender=Masc|Number=Plur|POS=VERB|Polarity=Pos|Tense=Pres|VerbForm=Part|Voice=Pass , Case=Acc|Definite=Ind|Degree=Pos|Gender=Masc|Number=Plur|POS=ADJ , POS=INTJ , Definite=Ind|Gender=Neut|NumForm=Word|NumType=Ord|POS=NUM , Case=Nom|Definite=Ind|Gender=Fem|Number=Plur|POS=VERB|Polarity=Pos|Tense=Pres|VerbForm=Part|Voice=Pass , Case=Loc|Definite=Ind|Gender=Fem|Number=Plur|POS=DET|PronType=Dem , Case=Dat|Definite=Ind|Gender=Fem|Number=Sing|POS=VERB|Polarity=Pos|Tense=Pres|VerbForm=Part|Voice=Act , `Case= |
📄 License
This project is licensed under the CC BY - SA 4.0
license.
Indonesian Roberta Base Posp Tagger
MIT
This is a POS tagging model fine-tuned based on the Indonesian RoBERTa model, trained on the indonlu dataset for Indonesian text POS tagging tasks.
Sequence Labeling
Transformers Other

I
w11wo
2.2M
7
Bert Base NER
MIT
BERT fine-tuned named entity recognition model capable of identifying four entity types: Location (LOC), Organization (ORG), Person (PER), and Miscellaneous (MISC)
Sequence Labeling English
B
dslim
1.8M
592
Deid Roberta I2b2
MIT
This model is a sequence labeling model fine-tuned on RoBERTa, designed to identify and remove Protected Health Information (PHI/PII) from medical records.
Sequence Labeling
Transformers Supports Multiple Languages

D
obi
1.1M
33
Ner English Fast
Flair's built-in fast English 4-class named entity recognition model, based on Flair embeddings and LSTM-CRF architecture, achieving an F1 score of 92.92 on the CoNLL-03 dataset.
Sequence Labeling
PyTorch English
N
flair
978.01k
24
French Camembert Postag Model
French POS tagging model based on Camembert-base, trained using the free-french-treebank dataset
Sequence Labeling
Transformers French

F
gilf
950.03k
9
Xlm Roberta Large Ner Spanish
A Spanish named entity recognition model fine-tuned based on the XLM-Roberta-large architecture, with excellent performance on the CoNLL-2002 dataset.
Sequence Labeling
Transformers Spanish

X
MMG
767.35k
29
Nusabert Ner V1.3
MIT
Named entity recognition model fine-tuned on Indonesian NER tasks based on NusaBert-v1.3
Sequence Labeling
Transformers Other

N
cahya
759.09k
3
Ner English Large
Flair framework's built-in large English NER model for 4 entity types, utilizing document-level XLM-R embeddings and FLERT technique, achieving an F1 score of 94.36 on the CoNLL-03 dataset.
Sequence Labeling
PyTorch English
N
flair
749.04k
44
Punctuate All
MIT
A multilingual punctuation prediction model fine-tuned based on xlm-roberta-base, supporting automatic punctuation completion for 12 European languages
Sequence Labeling
Transformers

P
kredor
728.70k
20
Xlm Roberta Ner Japanese
MIT
Japanese named entity recognition model fine-tuned based on xlm-roberta-base
Sequence Labeling
Transformers Supports Multiple Languages

X
tsmatz
630.71k
25
Featured Recommended AI Models