
<a href="https://github.com/centre-for-humanities-computing/Dacy"><img src="https://centre-for-humanities-computing.github.io/DaCy/_static/icon.png" width="175" height="175" align="right" /></a>

# DaCy large

DaCy is a Danish language processing framework with state-of-the-art pipelines as well as functionality for analysing Danish pipelines.
DaCy's largest pipeline has achieved State-of-the-Art performance on parts-of-speech tagging and dependency 
parsing for Danish on the Danish Dependency treebank as well as competitive performance on named entity recognition, named entity disambiguation and coreference resolution. 
To read more check out the [DaCy repository](https://github.com/centre-for-humanities-computing/DaCy) for material on how to use DaCy and reproduce the results. 
DaCy also contains guides on usage of the package as well as behavioural test for biases and robustness of Danish NLP pipelines.


| Feature | Description |
| --- | --- |
| **Name** | `da_dacy_large_trf` |
| **Version** | `0.2.0` |
| **spaCy** | `>=3.5.2,<3.6.0` |
| **Default Pipeline** | `transformer`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser`, `ner`, `coref`, `span_resolver`, `span_cleaner`, `entity_linker` |
| **Components** | `transformer`, `tagger`, `morphologizer`, `trainable_lemmatizer`, `parser`, `ner`, `coref`, `span_resolver`, `span_cleaner`, `entity_linker` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [UD Danish DDT v2.11](https://github.com/UniversalDependencies/UD_Danish-DDT) (Johannsen, Anders; Martínez Alonso, Héctor; Plank, Barbara)<br />[DaNE](https://huggingface.co/datasets/dane) (Rasmus Hvingelby, Amalie B. Pauli, Maria Barrett, Christina Rosted, Lasse M. Lidegaard, Anders Søgaard)<br />[DaCoref](https://huggingface.co/datasets/alexandrainst/dacoref) (Buch-Kromann, Matthias)<br />[DaNED](https://danlp-alexandra.readthedocs.io/en/stable/docs/datasets.html#daned) (Barrett, M. J., Lam, H., Wu, M., Lacroix, O., Plank, B., & Søgaard, A.)<br />[chcaa/dfm-encoder-large-v1](https://huggingface.co/chcaa/dfm-encoder-large-v1) (The Danish Foundation Models team) |
| **License** | `Apache-2.0` |
| **Author** | [Kenneth Enevoldsen](https://chcaa.io/#/) |

### Label Scheme

<details>

<summary>View label scheme (211 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` |
| **`morphologizer`** | `AdpType=Prep\|POS=ADP`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=PROPN`, `Definite=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=SCONJ`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=ADV`, `Number=Plur\|POS=DET\|PronType=Dem`, `Degree=Pos\|Number=Plur\|POS=ADJ`, `Definite=Ind\|Gender=Com\|Number=Plur\|POS=NOUN`, `POS=PUNCT`, `NumType=Ord\|POS=ADJ`, `POS=CCONJ`, `Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Sup\|POS=ADV`, `Degree=Pos\|POS=ADV`, `Gender=Com\|Number=Sing\|POS=DET\|PronType=Ind`, `Number=Plur\|POS=DET\|PronType=Ind`, `POS=ADP`, `POS=ADV\|PartType=Inf`, `Case=Nom\|Gender=Com\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=ADP\|PartType=Inf`, `Definite=Ind\|Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `NumType=Card\|POS=NUM`, `Degree=Pos\|POS=ADJ`, `Definite=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `POS=PART\|PartType=Inf`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Definite=Def\|Gender=Com\|Number=Plur\|POS=NOUN`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=VERB\|Tense=Pres\|VerbForm=Part`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Def\|Gender=Com\|Number=Sing\|POS=NOUN`, `Definite=Def\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=AUX\|VerbForm=Inf\|Voice=Act`, `Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Degree=Cmp\|POS=ADJ`, `POS=PRON\|PartType=Inf`, `Definite=Ind\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Com\|POS=PRON\|PronType=Ind`, `Number=Plur\|POS=PRON\|PronType=Ind`, `POS=INTJ`, `Gender=Com\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Degree=Cmp\|POS=ADV`, `Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Case=Acc\|Gender=Com\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|POS=PROPN`, `Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Com\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Definite=Def\|Degree=Sup\|POS=ADJ`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Definite=Def\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `POS=PRON\|PronType=Dem`, `Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `Number=Plur\|POS=NUM`, `POS=VERB\|VerbForm=Inf\|Voice=Pass`, `Definite=Def\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Gender=Com\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=PRON`, `Definite=Ind\|Number=Sing\|POS=NOUN`, `Definite=Ind\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `Foreign=Yes\|POS=ADV`, `POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Gender=Com\|Number=Plur\|POS=NOUN`, `Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Gender=Com\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Com\|Number=Plur\|POS=NOUN`, `Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Degree=Sup\|POS=ADJ`, `Degree=Pos\|Number=Sing\|POS=ADJ`, `Mood=Imp\|POS=VERB`, `Case=Nom\|Gender=Com\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Case=Acc\|Gender=Com\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `POS=X`, `Case=Gen\|Definite=Def\|Gender=Com\|Number=Plur\|POS=NOUN`, `Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Com\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Plur\|POS=PRON\|PronType=Int,Rel`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Degree=Cmp\|Number=Plur\|POS=ADJ`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Com\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Case=Nom\|Gender=Com\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Com\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Com\|POS=PRON\|PronType=Int,Rel`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `POS=VERB\|VerbForm=Ger`, `Gender=Com\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Gen\|POS=PRON\|PronType=Int,Rel`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Abbr=Yes\|POS=X`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|Number=Plur\|POS=NOUN`, `Foreign=Yes\|POS=X`, `Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=Nom\|Gender=Com\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Degree=Cmp\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Com\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=PRON\|PronType=Rcp`, `POS=DET\|Person=2\|Polite=Form\|Poss=Yes\|PronType=Prs`, `POS=SYM`, `POS=DET\|PronType=Dem`, `Gender=Com\|Number=Sing\|POS=NUM`, `Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Degree=Abs\|POS=ADJ`, `POS=VERB\|Tense=Pres`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NUM`, `Degree=Abs\|POS=ADV`, `Case=Gen\|Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Gender=Com\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Ind\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Number[psor]=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|POS=NOUN`, `Case=Gen\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=NUM`, `Definite=Def\|Number=Plur\|POS=NOUN`, `Case=Gen\|POS=NOUN`, `POS=AUX\|Tense=Pres\|VerbForm=Part` |
| **`parser`** | `ROOT`, `acl:relcl`, `advcl`, `advmod`, `advmod:lmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `dep`, `det`, `expl`, `fixed`, `flat`, `iobj`, `list`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nummod`, `obj`, `obl`, `obl:lmod`, `obl:tmod`, `punct`, `xcomp` |
| **`ner`** | `LOC`, `MISC`, `ORG`, `PER` |

</details>

### Accuracy

| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 99.92 |
| `TOKEN_P` | 99.70 |
| `TOKEN_R` | 99.77 |
| `TOKEN_F` | 99.74 |
| `SENTS_P` | 100.00 |
| `SENTS_R` | 100.00 |
| `SENTS_F` | 100.00 |
| `TAG_ACC` | 99.14 |
| `POS_ACC` | 99.08 |
| `MORPH_ACC` | 98.80 |
| `MORPH_MICRO_P` | 99.45 |
| `MORPH_MICRO_R` | 99.32 |
| `MORPH_MICRO_F` | 99.39 |
| `DEP_UAS` | 92.81 |
| `DEP_LAS` | 90.80 |
| `ENTS_P` | 88.58 |
| `ENTS_R` | 86.20 |
| `ENTS_F` | 87.38 |
| `LEMMA_ACC` | 95.89 |
| `COREF_LEA_F1` | 46.72 |
| `COREF_LEA_PRECISION` | 45.91 |
| `COREF_LEA_RECALL` | 47.56 |
| `NEL_SCORE` | 34.29 |
| `NEL_MICRO_P` | 84.00 |
| `NEL_MICRO_R` | 21.54 |
| `NEL_MICRO_F` | 34.29 |
| `NEL_MACRO_P` | 86.71 |
| `NEL_MACRO_R` | 24.70 |
| `NEL_MACRO_F` | 37.28 |



### Training
This model was trained using [spaCy](https://spacy.io) and logged to [Weights & Biases](https://wandb.ai/kenevoldsen/dacy-v0.2.0). You can find all the training logs [here](https://wandb.ai/kenevoldsen/dacy-v0.2.0).
{"language": ["da"], "license": "apache-2.0", "library_name": "spacy", "tags": ["spacy", "dacy", "danish", "token-classification", "pos tagging", "morphological analysis", "lemmatization", "dependency parsing", "named entity recognition", "coreference resolution", "named entity linking", "named entity disambiguation"], "datasets": ["universal_dependencies", "dane", "alexandrainst/dacoref"], "metrics": ["accuracy"], "model-index": [{"name": "da_dacy_large_trf-0.2.0", "results": [{"task": {"type": "token-classification", "name": "NER"}, "dataset": {"name": "DaNE", "type": "dane", "split": "test"}, "metrics": [{"type": "precision", "value": 0.8858195212, "name": "NER Precision"}, {"type": "recall", "value": 0.8620071685, "name": "NER Recall"}, {"type": "f_score", "value": 0.8737511353, "name": "NER F Score"}]}, {"task": {"type": "token-classification", "name": "TAG"}, "dataset": {"name": "UD Danish DDT", "type": "universal_dependencies", "config": "da_ddt", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.9913668347, "name": "TAG (XPOS) Accuracy"}, {"type": "accuracy", "value": 0.9908174469, "name": "POS (UPOS) Accuracy"}, {"type": "accuracy", "value": 0.9880227568, "name": "Morph (UFeats) Accuracy"}, {"type": "accuracy", "value": 0.9589423796, "name": "Lemma Accuracy"}, {"type": "f_score", "value": 0.9280885781, "name": "Unlabeled Attachment Score (UAS)"}, {"type": "f_score", "value": 0.9079997669, "name": "Labeled Attachment Score (LAS)"}, {"type": "f_score", "value": 1.0, "name": "Sentences F-Score"}]}, {"task": {"type": "coreference-resolution", "name": "coreference-resolution"}, "dataset": {"name": "DaCoref", "type": "alexandrainst/dacoref", "split": "custom"}, "metrics": [{"type": "f_score", "value": 0.4672143289, "name": "LEA"}]}, {"task": {"type": "coreference-resolution", "name": "coreference-resolution"}, "dataset": {"name": "DaNED", "type": "named-entity-linking", "split": "custom"}, "metrics": [{"type": "precision", "value": 0.84, "name": "Named entity Linking Precision"}, {"type": "recall", "value": 0.2153846154, "name": "Named entity Linking Recall"}, {"type": "f_score", "value": 0.3428571429, "name": "Named entity Linking F Score"}]}]}]}
spacy dacy danish token-classification pos tagging morphological analysis lemmatization dependency parsing named entity recognition coreference resolution named entity linking named entity disambiguation da dataset:universal_dependencies dataset:dane dataset:alexandrainst/dacoref license:apache-2.0 model-index region:us
TAGS
#spacy #dacy #danish #token-classification #pos tagging #morphological analysis #lemmatization #dependency parsing #named entity recognition #coreference resolution #named entity linking #named entity disambiguation #da #dataset-universal_dependencies #dataset-dane #dataset-alexandrainst/dacoref #license-apache-2.0 #model-index #region-us 

<a href="URL src="URL width="175" height="175" align="right" />


DaCy large
==========


DaCy is a Danish language processing framework with state-of-the-art pipelines as well as functionality for analysing Danish pipelines.
DaCy's largest pipeline has achieved State-of-the-Art performance on parts-of-speech tagging and dependency
parsing for Danish on the Danish Dependency treebank as well as competitive performance on named entity recognition, named entity disambiguation and coreference resolution.
To read more check out the DaCy repository for material on how to use DaCy and reproduce the results.
DaCy also contains guides on usage of the package as well as behavioural test for biases and robustness of Danish NLP pipelines.



### Label Scheme



View label scheme (211 labels for 4 components)


### Accuracy



### Training


This model was trained using spaCy and logged to Weights & Biases. You can find all the training logs here.



### Label Scheme



View label scheme (211 labels for 4 components) ### Accuracy ### Training


This model was trained using spaCy and logged to Weights & Biases. You can find all the training logs here.
TAGS
#spacy #dacy #danish #token-classification #pos tagging #morphological analysis #lemmatization #dependency parsing #named entity recognition #coreference resolution #named entity linking #named entity disambiguation #da #dataset-universal_dependencies #dataset-dane #dataset-alexandrainst/dacoref #license-apache-2.0 #model-index #region-us 
 ### Label Scheme



View label scheme (211 labels for 4 components) ### Accuracy ### Training


This model was trained using spaCy and logged to Weights & Biases. You can find all the training logs here.
passage: TAGS
#spacy #dacy #danish #token-classification #pos tagging #morphological analysis #lemmatization #dependency parsing #named entity recognition #coreference resolution #named entity linking #named entity disambiguation #da #dataset-universal_dependencies #dataset-dane #dataset-alexandrainst/dacoref #license-apache-2.0 #model-index #region-us 
### Label Scheme



View label scheme (211 labels for 4 components)### Accuracy### Training


This model was trained using spaCy and logged to Weights & Biases. You can find all the training logs here.