readme: ---
  tags:
  - spacy
  - token-classification
  - text-classification
  language:
  - es
  license: mit
  model-index:
  - name: es_bsc_demo_md
    results:
    - task:
        name: TAG
        type: token-classification
      metrics:
      - name: TAG (XPOS) Accuracy
        type: accuracy
        value: 0.9538757026
    - task:
        name: POS
        type: token-classification
      metrics:
      - name: POS (UPOS) Accuracy
        type: accuracy
        value: 0.9860176075
    - task:
        name: MORPH
        type: token-classification
      metrics:
      - name: Morph (UFeats) Accuracy
        type: accuracy
        value: 0.9810269013
    - task:
        name: LEMMA
        type: token-classification
      metrics:
      - name: Lemma Accuracy
        type: accuracy
        value: 0.9797689766
    - task:
        name: UNLABELED_DEPENDENCIES
        type: token-classification
      metrics:
      - name: Unlabeled Attachment Score (UAS)
        type: f_score
        value: 0.9125813681
    - task:
        name: LABELED_DEPENDENCIES
        type: token-classification
      metrics:
      - name: Labeled Attachment Score (LAS)
        type: f_score
        value: 0.880866503
    - task:
        name: SENTS
        type: token-classification
      metrics:
      - name: Sentences F-Score
        type: f_score
        value: 0.9595898673
  
  widget:      
  - text: "El Fútbol Club Barcelona, conocido popularmente como Barça, es una entidad polideportiva con sede en Barcelona, España."
  
  ---
  To install this model:
  
  pip install https://huggingface.co/PlanTL-GOB-ES/es_bsc_demo_md/resolve/main/es_bsc_demo_md-any-py3-none-any.whl
  
  Spanish light weight pipeline by BSC. Components: floret static vectors, morphologizer, parser,  attribute_ruler, lemmatizer, text classification.
  
  | Feature | Description |
  | --- | --- |
  | **Name** | `es_bsc_demo_md` |
  | **Version** | `3.4.1` |
  | **spaCy** | `>=3.4.1,<3.5.0` |
  | **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `lemmatizer`, `parser`, `textcat` |
  | **Components** | `tok2vec`, `tagger`, `morphologizer`, `lemmatizer`, `parser`, `textcat` |
  | **Vectors** | -1 keys, 50000 unique vectors (300 dimensions) |
  | **Sources** | [UD Spanish AnCora v2.10](https://github.com/UniversalDependencies/UD_Spanish-AnCora) (Martínez Alonso, Héctor; Zeman, Daniel)<br /> [Spanish floret embeddings from BNE corpus] (https://zenodo.org/record/7314098) <br /> |
  | **License** | `mit` |
  | **Author** | [Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)](https://huggingface.co/PlanTL-GOB-ES/es_bsc_demo_md) |
  | **Copyright** | Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022) |
  | **Funding** | This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL |
  
  ### Label Scheme
  
  <details>
  
  <summary>View label scheme (734 labels for 4 components)</summary>
  
  | Component | Labels |
  | --- | --- |
  | **`tagger`** | `ADJ`, `ADP`, `ADV`, `AUX`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PRON`, `PROPN`, `PUNCT`, `SCONJ`, `SYM`, `VERB`, `X`, `ao0fp0`, `ao0fs0`, `ao0mp0`, `ao0ms0`, `aq0000`, `aq00p0`, `aq00s0`, `aq0cc0`, `aq0cn0`, `aq0cp0`, `aq0cs0`, `aq0fp0`, `aq0fpp`, `aq0fs0`, `aq0fsp`, `aq0fsp-B2`, `aq0mn0`, `aq0mp0`, `aq0mpp`, `aq0ms0`, `aq0msp`, `cc`, `cs`, `da0fp0`, `da0fs0`, `da0m00`, `da0mp0`, `da0ms0`, `da0ns0`, `dd0cp0`, `dd0cs0`, `dd0fp0`, `dd0fs0`, `dd0mp0`, `dd0ms0`, `de0cn0`, `di00p0`, `di0cp0`, `di0cs0`, `di0fp0`, `di0fs0`, `di0mp0`, `di0ms0`, `dn00p0`, `dn0cp0`, `dn0cs0`, `dn0fp0`, `dn0fs0`, `dn0mp0`, `dn0ms0`, `dp1cps`, `dp1css`, `dp1fpp`, `dp1fsp`, `dp1mpp`, `dp1msp`, `dp1mss`, `dp2cps`, `dp2css`, `dp2fpp`, `dp2fsp`, `dp3cp0`, `dp3cs0`, `dp3fs0`, `dp3mp0`, `dp3ms0`, `dt0cn0`, `dt0fs0`, `dt0ms0`, `faa`, `fat`, `fc`, `fd`, `fe`, `fg`, `fh`, `fia`, `fit`, `fp`, `fpa`, `fpt`, `fs`, `fx`, `fz`, `i`, `nc00000`, `nccn000`, `nccp000`, `nccs000`, `ncf0000`, `ncfn000`, `ncfp000`, `ncfs000`, `ncfs00a`, `ncmn000`, `ncmp000`, `ncms00`, `ncms000`, `np00000`, `np0000a`, `np0000l`, `np0000o`, `np0000p`, `p0000000`, `p010p000`, `p010s000`, `p020s000`, `p0300000`, `pd0cp000`, `pd0cs000`, `pd0fp000`, `pd0fs000`, `pd0mp000`, `pd0ms000`, `pd0ns000`, `pe000000`, `pi000000`, `pi00s000`, `pi0cp000`, `pi0cs000`, `pi0fp000`, `pi0fs000`, `pi0mp0`, `pi0mp000`, `pi0ms0`, `pi0ms000`, `pn0cp000`, `pn0cs000`, `pn0fp000`, `pn0fs000`, `pn0mp000`, `pn0ms000`, `pp1cn000`, `pp1cp000`, `pp1cs000`, `pp1csn00`, `pp1cso00`, `pp1fs000`, `pp1mp000`, `pp2cp000`, `pp2cp00p`, `pp2cs000`, `pp2cs00p`, `pp2csn00`, `pp2cso00`, `pp300000`, `pp30p000`, `pp30sa00`, `pp3cn000`, `pp3cna00`, `pp3cno00`, `pp3cpa00`, `pp3cpd00`, `pp3csa00`, `pp3csd00`, `pp3fp000`, `pp3fpa00`, `pp3fs000`, `pp3fsa00`, `pp3mp000`, `pp3mpa00`, `pp3ms000`, `pp3msa00`, `pp3ns000`, `pr00000`, `pr000000`, `pr0cn000`, `pr0cp000`, `pr0cs000`, `pr0fp000`, `pr0fs000`, `pr0mp000`, `pr0ms000`, `pt000000`, `pt0cp000`, `pt0cs000`, `pt0fp000`, `pt0mp000`, `pt0ms000`, `px1fp0p0`, `px1fs0p0`, `px1fs0s0`, `px1mp0p0`,