| Component | Labels |
| --- | --- |
| **`morphologizer`** | `POS=NOUN`, `POS=ADP`, `POS=VERB`, `POS=SCONJ`, `POS=AUX`, `POS=PUNCT`, `POS=PART`, `POS=DET`, `POS=NUM`, `POS=ADV`, `POS=PRON`, `POS=ADJ`, `POS=PROPN`, `POS=CCONJ`, `POS=SYM`, `POS=NOUN\|Polarity=Neg`, `POS=AUX\|Polarity=Neg`, `POS=INTJ`, `POS=SCONJ\|Polarity=Neg` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `aux`, `case`, `cc`, `ccomp`, `compound`, `cop`, `csubj`, `dep`, `det`, `dislocated`, `fixed`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `punct` |
| **`senter`** | `I`, `S` |
| **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `MOVEMENT`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PET_NAME`, `PHONE`, `PRODUCT`, `QUANTITY`, `TIME`, `TITLE_AFFIX`, `WORK_OF_ART` |


</details>


### Accuracy


| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 99.69 |
| `TOKEN_P` | 97.65 |
| `TOKEN_R` | 97.90 |
| `TOKEN_F` | 97.77 |
| `POS_ACC` | 97.36 |
| `MORPH_ACC` | 0.40 |
| `MORPH_MICRO_P` | 34.01 |
| `MORPH_MICRO_R` | 98.04 |
| `MORPH_MICRO_F` | 50.51 |
| `SENTS_P` | 98.62 |
| `SENTS_R` | 98.82 |
| `SENTS_F` | 98.72 |
| `DEP_UAS` | 92.14 |
| `DEP_LAS` | 90.81 |
| `TAG_ACC` | 97.16 |
| `LEMMA_ACC` | 96.59 |
| `ENTS_P` | 74.02 |
| `ENTS_R` | 69.18 |
| `ENTS_F` | 71.52 |


model name: ja_core_news_lg


model author: spacy


task: token-classification


tags: ja, spacy, token-classification, license:cc-by-sa-4.0, model-index


tags:


- spacy
- token-classification
language:
- lt
license: cc-by-sa-4.0
model-index:
- name: lt_core_news_md
results:
  - task:
name: NER
type: token-classification
metrics:
    - name: NER Precision
type: precision
value: 0.750907441
    - name: NER Recall
type: recall
value: 0.827913957
    - name: NER F Score
type: f_score
value: 0.7875327147
  - task:
name: POS
type: token-classification
metrics:
    - name: POS Accuracy
type: accuracy
value: 0.8630012545
  - task:
name: SENTER
type: token-classification
metrics:
    - name: SENTER Precision
type: precision
value: 0.8262295082
    - name: SENTER Recall
type: recall
value: 0.8168557536
    - name: SENTER F Score
type: f_score
value: 0.8215158924
  - task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
    - name: Unlabeled Dependencies Accuracy
type: accuracy
value: 0.7404241849
  - task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
    - name: Labeled Dependencies Accuracy
type: accuracy
value: 0.7404241849


### Details: https://spacy.io/models/lt#lt_core_news_md


Lithuanian pipeline optimized for CPU. Components: tok2vec, morphologizer, tagger, parser, senter, ner, attribute_ruler, lemmatizer.


| Feature | Description |
| --- | --- |
| **Name** | `lt_core_news_md` |
| **Version** | `3.2.0` |
| **spaCy** | `>=3.2.0,<3.3.0` |
| **Default Pipeline** | `tok2vec`, `morphologizer`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
| **Components** | `tok2vec`, `morphologizer`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
| **Vectors** | 500000 keys, 20000 unique vectors (300 dimensions) |
| **Sources** | [UD Lithuanian ALKSNIS v2.8](https://github.com/UniversalDependencies/UD_Lithuanian-ALKSNIS) (Utka, Andrius; Rimkutė, Erika; Bielinskienė, Agnė; Kovalevskaitė, Jolanta; Boizou, Loïc; Aleksandravičiūtė, Gabrielė; Brokaitė, Kristina; Zeman, Daniel; Perkova, Natalia; Griciūtė, Bernadeta)
[TokenMill NER Corpus](https://www.tokenmill.lt) (TokenMill)
[Lemmatization Lists](https://github.com/michmech/lemmatization-lists/) (Michal Měchura)
[Explosion fastText Vectors (cbow, OSCAR Common Crawl + Wikipedia)](https://spacy.io) (Explosion) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |


### Label Scheme


<details>


<summary>View label scheme (1669 labels for 5 components)</summary>