Instructions to use ipipan/pl_nask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use ipipan/pl_nask with spaCy:
!pip install https://huggingface.co/ipipan/pl_nask/resolve/main/pl_nask-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("pl_nask") # Importing as module. import pl_nask nlp = pl_nask.load() - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - spacy | |
| - token-classification | |
| language: | |
| - pl | |
| license: gpl-3.0 | |
| model-index: | |
| - name: pl_nask | |
| results: | |
| - task: | |
| name: NER | |
| type: token-classification | |
| metrics: | |
| - name: NER Precision | |
| type: precision | |
| value: 0.9092043682 | |
| - name: NER Recall | |
| type: recall | |
| value: 0.9072229141 | |
| - name: NER F Score | |
| type: f_score | |
| value: 0.9082125604 | |
| - task: | |
| name: TAG | |
| type: token-classification | |
| metrics: | |
| - name: TAG (XPOS) Accuracy | |
| type: accuracy | |
| value: 0.9626419163, | |
| - task: | |
| name: UNLABELED_DEPENDENCIES | |
| type: token-classification | |
| metrics: | |
| - name: Unlabeled Attachment Score (UAS) | |
| type: f_score | |
| value: 0.9290706682 | |
| - task: | |
| name: LABELED_DEPENDENCIES | |
| type: token-classification | |
| metrics: | |
| - name: Labeled Attachment Score (LAS) | |
| type: f_score | |
| value: 0.8765904858 | |
| - task: | |
| name: SENTS | |
| type: token-classification | |
| metrics: | |
| - name: Sentences F-Score | |
| type: f_score | |
| value: 0.9918545179 | |
| ### Details: https://github.com/ipipan/spacy-pl-trf | |
| Polish transformer pipeline (herbert-base-cased). Components: transformer, tagger, parser, ner, morfeusz. | |
| | Feature | Description | | |
| | --- | --- | | |
| | **Name** | `pl_nask` | | |
| | **Version** | `0.0.7` | | |
| | **spaCy** | `>=3.5.0,<3.6.0` | | |
| | **Default Pipeline** | `transformer`, `tagger`, `morphologizer`, `parser`, `ner`, `morfeusz` | | |
| | **Components** | `transformer`, `tagger`, `morphologizer`, `parser`, `ner`, `morfeusz` | | |
| | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | |
| | **Sources** | [Morfeusz2](http://morfeusz.sgjp.pl/en) 2-clauseBSD License<br /> [NKJP1M](http://nkjp.pl/) CC-BY<br /> [NKJP1MUD](http://git.nlp.ipipan.waw.pl/alina/PDBUD/tree/master) GNU GPL v.3<br /> [Herbert-base-cased](https://huggingface.co/allegro/herbert-base-cased) CC-BY<br /> [PDB](http://zil.ipipan.waw.pl/PDB) CC BY-NC-SA 4.0<br /> | | |
| | **License** | `GNU GPL 3.0` | | |
| | **Author** | [Ryszard Tuora ICS PAS](http://zil.ipipan.waw.pl/) | | |
| ### Label Scheme | |
| <details> | |
| <summary>View label scheme</summary> | |
| | Component | Labels | | |
| | --- | --- | | |
| | **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advcl:cmpr`, `advcl:relcl`, `advmod`, `advmod:arg`, `advmod:emph`, `advmod:neg`, `amod`, `amod:flat`, `appos`, `aux`, `aux:cnd`, `aux:imp`, `aux:pass`, `case`, `cc`, `cc:preconj`, `ccomp`, `ccomp:cleft`, `ccomp:obj`, `conj`, `cop`, `csubj`, `dep`, `det`, `det:numgov`, `det:nummod`, `det:poss`, `discourse:intj`, `expl:pv`, `fixed`, `flat`, `flat:foreign`, `iobj`, `list`, `mark`, `nmod`, `nmod:arg`, `nmod:flat`, `nmod:poss`, `nsubj`, `nsubj:pass`, `nummod`, `nummod:flat`, `nummod:gov`, `obj`, `obl`, `obl:agent`, `obl:arg`, `obl:cmpr`, `obl:orphan`, `orphan`, `parataxis:insert`, `parataxis:obj`, `punct`, `vocative`, `xcomp`, `xcomp:cleft`, `xcomp:pred`, `xcomp:subj` | | |
| | **`ner`** | `DATE`, `GEOGNAME`, `ORGNAME`, `PERSNAME`, `PLACENAME`, `TIME` | | |
| </details> | |
| ### Accuracy | |
| | Type | Score | | |
| | --- | --- | | |
| | `TAG_ACC` | 96.26 | | |
| | `POS_ACC` | 98.18 | | |
| | `MORPH_ACC` | 96.61 | | |
| | `SENTS_P` | 99.13 | | |
| | `SENTS_R` | 99.24 | | |
| | `SENTS_F` | 99.19 | | |
| | `DEP_UAS` | 92.91 | | |
| | `DEP_LAS` | 87.66 | | |
| | `ENTS_P` | 90.92 | | |
| | `ENTS_R` | 90.72 | | |
| | `ENTS_F` | 90.82 | | |