%20--%3e%3cdefs%3e%3cstyle%3e%20.st0%20{%20fill:%20%23061b40;%20}%20.st1%20{%20fill:%20%23306af1;%20}%20.st2%20{%20fill:%20%235ce5cf;%20}%20%3c/style%3e%3c/defs%3e%3cg%3e%3cpath%20class='st0'%20d='M55,10.5h9v3h-9v9h12V7.5h-12v3ZM64,19.5h-6v-3h6v3Z'/%3e%3cpolygon%20class='st0'%20points='69%2016.5%2078%2016.5%2078%2019.5%2069%2019.5%2069%2022.5%2081%2022.5%2081%2013.5%2072%2013.5%2072%2010.5%2081%2010.5%2081%207.5%2069%207.5%2069%2016.5'/%3e%3cpolygon%20class='st0'%20points='95%2010.5%2095%207.5%2083%207.5%2083%2022.5%2095%2022.5%2095%2019.5%2086%2019.5%2086%2016.5%2095%2016.5%2095%2013.5%2086%2013.5%2086%2010.5%2095%2010.5'/%3e%3cpath%20class='st0'%20d='M40,1.5v21h11.6l1.4-1.4v-7.6h0c0,0-1.4-1.5-1.4-1.5l1.4-1.4V2.9l-1.4-1.4h-11.6ZM50,19.5h-7v-6h7v6ZM50,10.5h-7v-6h7v6Z'/%3e%3c/g%3e%3cpath%20class='st1'%20d='M23.1,24L14.7,4.8l-4.9,11.2h3.8l-1.8,4H3.3L12.1,0H2C.9,0,0,.9,0,2v20c0,1.1.9,2,2,2h21.1Z'/%3e%3cpath%20class='st2'%20d='M34,0h-16.8l10.6,24h6.2c1.1,0,2-.9,2-2V2C36,.9,35.1,0,34,0ZM32.5,20h-4V4h4v16Z'/%3e%3c/svg%3e)
EN
Pt Core News Lg
A CPU-optimized Portuguese processing pipeline, including tokenization, POS tagging, dependency parsing, named entity recognition, and more.
Downloads 57
Release Time : 3/2/2022
Model Overview
This is a large natural language processing model for Portuguese, offering comprehensive text analysis capabilities, including POS tagging, dependency parsing, named entity recognition, etc. The model is optimized for CPU usage.
Model Features
CPU Optimization
Specially optimized for CPU usage, no high-end GPU required
Comprehensive NLP Features
Provides a complete natural language processing pipeline from tokenization to named entity recognition
High Accuracy
Excellent performance on Portuguese processing tasks, such as NER F1 score of 0.903
Pre-trained Word Vectors
Includes 500,000 pre-trained word vectors to enhance processing effectiveness
Model Capabilities
POS tagging
Dependency parsing
Named entity recognition
Lemmatization
Morphological analysis
Sentence boundary detection
Use Cases
Text Analysis
Portuguese Document Processing
Automatically analyzes POS, syntactic structure, and named entities in Portuguese documents
Accurately identifies entities and grammatical relationships in documents
Information Extraction
News Analysis
Extracts entity information such as person names, locations, and organizations from Portuguese news
NER precision rate of 0.902
🚀 pt_core_news_lg
The pt_core_news_lg is a Portuguese language processing pipeline optimized for CPU, offering various token - classification tasks with high performance.
📚 Documentation
Details: https://spacy.io/models/pt#pt_core_news_lg
The Portuguese pipeline is optimized for CPU. Components include: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler.
| Property | Details |
|---|---|
| Name | pt_core_news_lg |
| Version | 3.7.0 |
| spaCy | >=3.7.0,<3.8.0 |
| Default Pipeline | tok2vec, morphologizer, parser, lemmatizer, attribute_ruler, ner |
| Components | tok2vec, morphologizer, parser, lemmatizer, senter, attribute_ruler, ner |
| Vectors | 500000 keys, 500000 unique vectors (300 dimensions) |
| Sources | UD Portuguese Bosque v2.8 (Rademaker, Alexandre; Freitas, Cláudia; de Souza, Elvis; Silveira, Aline; Cavalcanti, Tatiana; Evelyn, Wograine; Rocha, Luisa; Soares-Bastos, Isabela; Bick, Eckhard; Chalub, Fabricio; Paulino-Passos, Guilherme; Real, Livy; de Paiva, Valeria; Zeman, Daniel; Popel, Martin; Mareček, David; Silveira, Natalia; Martins, André) WikiNER (Joel Nothman, Nicky Ringland, Will Radford, Tara Murphy, James R Curran) Explosion fastText Vectors (cbow, OSCAR Common Crawl + Wikipedia) (Explosion) |
| License | CC BY - SA 4.0 |
| Author | Explosion |
Model Performance
The pt_core_news_lg model has been evaluated on several token - classification tasks, and the following are the performance metrics:
| Task | Metric | Value |
|---|---|---|
| NER | NER Precision | 0.9016596045 |
| NER | NER Recall | 0.9045519847 |
| NER | NER F Score | 0.9031034788 |
| TAG | TAG (XPOS) Accuracy | 0.8951305295 |
| POS | POS (UPOS) Accuracy | 0.9694357755 |
| MORPH | Morph (UFeats) Accuracy | 0.958640844 |
| LEMMA | Lemma Accuracy | 0.9730077757 |
| UNLABELED_DEPENDENCIES | Unlabeled Attachment Score (UAS) | 0.9021776702 |
| LABELED_DEPENDENCIES | Labeled Attachment Score (LAS) | 0.8624265468 |
| SENTS | Sentences F - Score | 0.9512921022 |
Label Scheme
View label scheme (590 labels for 3 components)
| Component | Labels |
|---|---|
morphologizer |
Definite=Ind|Gender=Masc|Number=Sing|POS=DET|PronType=Art, Gender=Masc|Number=Sing|POS=NOUN, Gender=Masc|Number=Sing|POS=ADJ, Definite=Def|Gender=Masc|Number=Sing|POS=DET|PronType=Art, Gender=Masc|Number=Sing|POS=PROPN, Number=Sing|POS=PROPN, Mood=Ind|Number=Sing|POS=AUX|Person=3|Tense=Pres|VerbForm=Fin, Gender=Masc|Number=Plur|POS=NOUN, Definite=Def|POS=ADP|PronType=Art, Gender=Fem|Number=Sing|POS=NOUN, Gender=Fem|Number=Sing|POS=ADJ, POS=PUNCT, NumType=Card|POS=NUM, POS=ADV, Gender=Fem|Number=Plur|POS=ADJ, Gender=Fem|Number=Plur|POS=NOUN, Definite=Def|Gender=Masc|Number=Sing|POS=ADP|PronType=Art, Gender=Fem|Number=Sing|POS=PROPN, Gender=Fem|Number=Sing|POS=VERB|VerbForm=Part, POS=ADP, POS=PRON|PronType=Rel, Mood=Ind|Number=Sing|POS=VERB|Person=3|Tense=Pres|VerbForm=Fin, POS=SCONJ, POS=VERB|VerbForm=Inf, Definite=Def|Gender=Masc|Number=Plur|POS=DET|PronType=Art, Gender=Masc|Number=Plur|POS=ADJ, POS=CCONJ, Definite=Def|Gender=Fem|Number=Plur|POS=DET|PronType=Art, Definite=Def|Gender=Fem|Number=Sing|POS=DET|PronType=Art, Definite=Ind|Gender=Fem|Number=Sing|POS=DET|PronType=Art, Gender=Masc|Number=Sing|POS=DET|PronType=Ind, Mood=Sub|Number=Sing|POS=AUX|Person=3|Tense=Pres|VerbForm=Fin, Definite=Def|Gender=Masc|Number=Plur|POS=ADP|PronType=Art, Gender=Masc|Number=Plur|POS=PRON|PronType=Rel, Gender=Fem|Number=Sing|POS=PRON|Person=3|PronType=Prs, Mood=Ind|Number=Plur|POS=VERB|Person=3|Tense=Pres|VerbForm=Fin, POS=ADV|Polarity=Neg, Gender=Masc|Number=Sing|POS=DET|PronType=Art, POS=X, Gender=Masc|Number=Plur|POS=PRON|PronType=Dem, Gender=Fem|Number=Plur|POS=DET|PronType=Ind, Mood=Ind|Number=Plur|POS=VERB|Person=1|Tense=Pres|VerbForm=Fin, Gender=Masc|Number=Plur|POS=PRON|PronType=Tot, Case=Acc|Gender=Masc|Mood=Ind|Number=Plur|POS=VERB|Person=1|PronType=Prs|Tense=Pres|VerbForm=Fin, Number=Sing|POS=CCONJ, Gender=Masc|Number=Sing|POS=VERB|VerbForm=Part, Gender=Masc|Number=Plur|POS=DET|PronType=Dem, Case=Acc|Gender=Masc|Number=Plur|POS=VERB|Person=3|PronType=Prs|VerbForm=Inf, Gender=Masc|Number=Sing|POS=DET|PronType=Dem, Gender=Masc|Number=Sing|POS=PRON|PronType=Rel, Case=Acc|Gender=Fem|Number=Plur|POS=VERB|Person=3|PronType=Prs|VerbForm=Inf, Gender=Fem|Number=Plur|POS=PRON|PronType=Ind, Gender=Masc|Number=Plur|POS=DET|PronType=Prs, Case=Acc|Gender=Masc|Mood=Sub|Number=Plur|POS=VERB|Person=3|PronType=Prs|Tense=Pres|VerbForm=Fin, Number=Plur|POS=NOUN, Mood=Sub|Number=Plur|POS=VERB|Person=3|Tense=Fut|VerbForm=Fin, POS=AUX|VerbForm=Inf, Gender=Fem|Number=Plur|POS=VERB|VerbForm=Part|Voice=Pass, Case=Nom|Gender=Masc|Number=Plur|POS=PRON|Person=3|PronType=Prs, Gender=Masc|Number=Sing|POS=ADP|PronType=Dem, Gender=Masc|Number=Sing|POS=PRON|PronType=Dem, POS=VERB|VerbForm=Ger, Mood=Ind|Number=Plur|POS=AUX|Person=3|Tense=Pres|VerbForm=Fin, Gender=Masc|Number=Plur|POS=VERB|VerbForm=Part|Voice=Pass, Gender=Masc|Number=Plur|POS=PROPN, Number=Plur|POS=AUX|Person=3|VerbForm=Inf, Gender=Fem|Number=Sing|POS=PRON|PronType=Dem, Mood=Ind|Number=Sing|POS=VERB|Person=3|Tense=Fut|VerbForm=Fin, Gender=Masc|Number=Plur|POS=PRON|PronType=Ind, Mood=Ind|Number=Plur|POS=VERB|Person=3|Tense=Past|VerbForm=Fin, Definite=Def|Gender=Masc|Number=Sing|POS=PRON|PronType=Art, POS=VERB|VerbForm=Part, Gender=Masc|NumType=Ord|Number=Sing|POS=ADJ, Mood=Ind|Number=Sing|POS=VERB|Person=3|Tense=Past|VerbForm=Fin, Gender=Fem|Number=Sing|POS=DET|PronType=Dem, Definite=Ind|Gender=Fem|Number=Sing|POS=ADP|PronType=Art, Gender=Fem|Number=Sing|POS=PRON|PronType=Rel, Mood=Sub|Number=Sing|POS=VERB|Person=3|Tense=Pres|VerbForm=Fin, Definite=Def|Gender=Fem|Number=Sing|POS=ADP|PronType=Art, Mood=Ind|Number=Sing|POS=AUX|Person=3|Tense=Past|VerbForm=Fin, Case=Acc|Gender=Masc|Mood=Ind|Number=Sing|POS=VERB|Person=3|PronType=Prs|Tense=Pres|VerbForm=Fin, Case=Acc|Gender=Masc|Number=Plur|POS=PRON|Person=3|PronType=Prs, Gender=Masc|Number=Sing|POS=VERB|VerbForm=Part|Voice=Pass, Case=Dat|Gender=Masc|Number=Sing|POS=PRON|Person=3|PronType=Prs, Mood=Ind|Number=Sing|POS=VERB|Person=1|Tense=Pres|VerbForm=Fin, Case=Nom|Number=Plur|POS=PRON|Person=1|PronType=Prs, Mood=Sub|Number=Plur|POS=VERB|Person=1|Tense=Imp|VerbForm=Fin, Mood=Sub|Number=Sing|POS=VERB|Person=3|Tense=Fut|VerbForm=Fin, Gender=Fem|NumType=Ord|Number=Plur|POS=ADJ, Gender=Fem|Number=Plur|POS=DET|PronType=Prs, Gender=Masc|Number=Plur|POS=DET|PronType=Ind, Gender=Masc|NumType=Ord|Number=Plur|POS=ADJ, Case=Acc|Gender=Masc|Mood=Ind|Number=Plur|POS=VERB|Person=3|PronType=Prs|Tense=Pres|VerbForm=Fin, NumType=Ord|POS=ADJ, Definite=Def|Gender=Masc|Number=Sing|POS=DET|PronType=Dem, Case=Acc|Gender=Fem|Mood=Ind|Number=Sing|POS=VERB|Person=3|PronType=Prs|Tense=Pres|VerbForm=Fin, Gender=Masc|Number=Sing|POS=PRON|PronType=Ind, Number=Plur|POS=ADJ, Gender=Fem|Number=Sing|POS=VERB|VerbForm=Part|Voice=Pass, Mood=Ind|Number=Sing|POS=AUX|Person=3|Tense=Fut|VerbForm=Fin, Gender=Masc|Number=Sing|POS=SCONJ|PronType=Dem, Mood=Sub|Number=Sing|POS=AUX|Person=3|Tense=Fut|VerbForm=Fin, Gender=Fem|Number=Sing|POS=DET|PronType=Tot, Gender=Fem|Number=Sing|POS=PRON|PronType=Ind, Gender=Masc|Number=Plur|POS=VERB|VerbForm=Part, Gender=Fem|Number=Plur|POS=VERB|VerbForm=Part, Gender=Masc|NumType=Mult|Number=Sing|POS=NUM, Number=Sing|POS=PRON|PronType=Ind, Mood=Ind|Number=Plur|POS=AUX|Person=3|Tense=Past|VerbForm=Fin, Case=Acc|Gender=Fem|Number=Plur|POS=PRON|Person=3|PronType=Prs, Mood=Cnd|Number=Sing|POS=VERB|Person=3|VerbForm=Fin, Gender=Fem|Number=Plur|POS=PRON|PronType=Rel, Number=Plur|POS=PRON|Person=1|PronType=Prs, Mood=Sub|Number=Plur|POS=VERB|Person=3|Tense=Pres|VerbForm=Fin, Case=Acc|Gender=Fem|Number=Sing|POS=PRON|Person=3|PronType=Prs, Gender=Masc|Number=Plur|POS=DET|PronType=Tot, Gender=Masc|Number=Sing|POS=PROPN|PronType=Art, Gender=Fem|Number=Sing|POS=DET|PronType=Prs, Case=Acc|Gender=Masc|Number=Sing|POS=PRON|Person=3|PronType=Prs, Number=Sing|POS=VERB|Person=3|VerbForm=Inf, Case=Nom|Gender=Masc|Number=Sing|POS=PRON|Person=1|PronType=Prs, Mood=Ind|Number=Sing|POS=AUX|Person=1|Tense=Pres|VerbForm=Fin, Mood=Ind|Number=Sing|POS=VERB|Person=1|Tense=Past|VerbForm=Fin, Case=Nom|Gender=Fem|Number=Sing|POS=PRON|Person=3|PronType=Prs, Case=Acc|Number=Plur|POS=PRON|Person=1|PronType=Prs, Mood=Sub|Number=Sing|POS=VERB|Person=3|Tense=Imp|VerbForm=Fin, Mood=Cnd|Number=Sing|POS=AUX|Person=3|VerbForm=Fin, POS=AUX|VerbForm=Part, POS=SPACE, Gender=Fem|Number=Sing|POS=DET|PronType=Ind, Case=Nom|Gender=Masc|Number=Sing|POS=PRON|Person=3|PronType=Prs, Mood=Ind|Number=Sing|POS=AUX|Person=1|Tense=Past|VerbForm=Fin, Case=Nom|Number=Sing|POS=PRON|Person=1|PronType=Prs, Number=Sing|POS=PRON|PronType=Rel, Number=Sing|POS=DET|PronType=Art, Definite=Def|Gender=Fem|Number=Plur|POS=ADP|PronType=Art, Mood=Ind|Number=Plur|POS=VERB|Person=3|Tense=Imp|VerbForm=Fin, Mood=Ind|Number=Sing|POS=AUX|Person=3|Tense=Imp|VerbForm=Fin, Gender=Masc|NumType=Frac|Number=Sing|POS=NUM, Gender=Masc|Number=Sing|POS=DET|PronType=Prs, Mood=Ind|Number=Sing|POS=VERB|Person=3|Tense=Imp|VerbForm=Fin, Mood=Ind|Number=Plur|POS=AUX|Person=3|Tense=Imp|VerbForm=Fin, Case=Acc|Gender=Masc|Mood=Ind|Number=Sing|POS=PRON|Person=3|PronType=Prs|Tense=Pres|VerbForm=Fin, Gender=Fem|Number=Sing|POS=ADP|PronType=Dem, Gender=Masc|Number=Plur|POS=DET|PronType=Art, Case=Acc|Gender=Masc|Number=Sing|POS=PRON|Person=1|PronType=Prs, Gender=Fem|NumType=Ord|Number=Sing|POS=ADJ, Case=Acc|Gender=Masc|Number=Sing|POS=VERB|Person=3|PronType=Prs|VerbForm=Inf, Number=Plur|POS=VERB|Person=3|VerbForm=Inf, Definite=Def|Gender=Masc|Number=Sing|POS=SCONJ|PronType=Art, Definite=Def|POS=SCONJ|PronType=Art, Gender=Masc|Number=Plur|POS=ADP|PronType=Art, Mood=Ind|Number=Sing|POS=VERB|Person=3|Tense=Pqp|VerbForm=Fin, Case=Acc|Gender=Masc|Mood=Ind|Number=Sing|POS=VERB|Person=3|PronType=Prs|Tense=Past|VerbForm=Fin, Gender=Fem|Number=Plur|POS=PRON|PronType=Dem, Gender=Fem|Number=Plur|POS=PROPN, Case=Acc|POS=PRON|PronType=Prs, Mood=Ind|Number=Plur|POS=VERB|Person=3|VerbForm=Fin, POS=AUX, Case=Acc|Mood=Ind|Number=Sing|POS=VERB|Person=3|PronType=Prs|Tense=Past|VerbForm=Fin, Gender=Masc|Number=Sing|POS=ADP|PronType=Art, Gender=Fem|Number=Sing|POS=ADP|PronType=Art, POS=INTJ, Case=Acc|Mood=Ind|Number=Sing|POS=VERB|Person=3|PronType=Prs|Tense=Pres|VerbForm=Fin, Number=Sing|POS=PRON|PronType=Int, Gender=Fem|Number=Sing|POS=DET|PronType=Rel, Gender=Masc|Number=Sing|POS=DET|PronType=Emp, Case=Acc|Mood=Sub|Number=Sing|POS=VERB|Person=3|PronType=Prs|Tense=Pres|VerbForm=Fin, Gender=Masc|POS=PRON|PronType=Ind, Gender=Fem|Number=Plur|POS=DET|PronType=Rel, Gender=Masc|Number=Sing|POS=PRON|Person=3|PronType=Prs, Definite=Ind|Gender=Masc|Number=Sing|POS=ADP|PronType=Art, Mood=Ind|Number=Sing|POS=AUX|Person=3|Tense=Pqp|VerbForm=Fin, Mood=Ind|Number=Sing|POS=VERB|Person=2|Tense=Past|VerbForm=Fin, Mood=Ind|Number=Sing|POS=AUX|Person=2|Tense=Pres|VerbForm=Fin, Case=Dat|Gender=Masc|Mood=Ind|Number=Sing|POS=VERB|Person=3|PronType=Prs|Tense=Pres|VerbForm=Fin, `Case=Acc|Gender=Fem|Mood=Ind|Numb |
📄 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
Transformers OtherI
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
Transformers Supports Multiple LanguagesD
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
PyTorch EnglishN
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
Transformers FrenchF
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
Transformers SpanishX
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
Transformers OtherN
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
PyTorch EnglishN
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
TransformersP
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
Transformers Supports Multiple LanguagesX
tsmatz
630.71k
25
Featured Recommended AI Models