Token Classification
spaCy
Danish
dacy
danish
pos tagging
morphological analysis
lemmatization
dependency parsing
named entity recognition
coreference resolution
named entity linking
named entity disambiguation
Eval Results (legacy)
Instructions to use chcaa/da_dacy_large_trf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use chcaa/da_dacy_large_trf with spaCy:
!pip install https://huggingface.co/chcaa/da_dacy_large_trf/resolve/main/da_dacy_large_trf-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("da_dacy_large_trf") # Importing as module. import da_dacy_large_trf nlp = da_dacy_large_trf.load() - Notebooks
- Google Colab
- Kaggle
Kenneth Enevoldsen commited on
Commit ·
963232f
1
Parent(s): fe85a1c
Added readme
Browse files
README.md
CHANGED
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| 1 |
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| 2 |
<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>
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+
---
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+
tags:
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+
- spacy
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- dacy
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+
- danish
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+
- token-classification
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- pos tagging
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- morphological analysis
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- lemmatization
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+
- dependency parsing
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+
- named entity recognition
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+
- coreference resolution
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+
- named entity linking
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+
- named entity disambiguation
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+
language:
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+
- da
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+
license: apache-2.0
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+
model-index:
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+
- name: da_dacy_large_trf-0.2.0
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+
results:
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+
- task:
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+
name: NER
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+
type: token-classification
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+
metrics:
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- name: NER Precision
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+
type: precision
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value: 0.8858195212
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+
- name: NER Recall
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type: recall
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value: 0.8620071685
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+
- name: NER F Score
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+
type: f_score
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| 33 |
+
value: 0.8737511353
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+
dataset:
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name: DaNE
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split: test
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type: dane
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- task:
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name: TAG
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type: token-classification
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metrics:
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- name: TAG (XPOS) Accuracy
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type: accuracy
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value: 0.9913668347
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| 45 |
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dataset:
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name: UD Danish DDT
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split: test
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type: universal_dependencies
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config: da_ddt
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- task:
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name: POS
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type: token-classification
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metrics:
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- name: POS (UPOS) Accuracy
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type: accuracy
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value: 0.9908174469
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+
dataset:
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name: UD Danish DDT
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split: test
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type: universal_dependencies
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config: da_ddt
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- task:
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name: MORPH
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type: token-classification
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metrics:
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- name: Morph (UFeats) Accuracy
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type: accuracy
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value: 0.9880227568
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dataset:
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name: UD Danish DDT
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split: test
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type: universal_dependencies
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config: da_ddt
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- task:
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name: LEMMA
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type: token-classification
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metrics:
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- name: Lemma Accuracy
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type: accuracy
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value: 0.9589423796
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dataset:
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name: UD Danish DDT
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split: test
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type: universal_dependencies
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config: da_ddt
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- task:
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name: UNLABELED_DEPENDENCIES
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type: token-classification
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metrics:
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- name: Unlabeled Attachment Score (UAS)
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type: f_score
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value: 0.9280885781
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dataset:
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name: UD Danish DDT
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split: test
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type: universal_dependencies
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config: da_ddt
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- task:
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name: LABELED_DEPENDENCIES
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type: token-classification
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metrics:
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- name: Labeled Attachment Score (LAS)
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type: f_score
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value: 0.9079997669
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dataset:
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name: UD Danish DDT
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split: test
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type: universal_dependencies
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config: da_ddt
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- task:
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name: SENTS
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type: token-classification
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metrics:
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- name: Sentences F-Score
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type: f_score
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value: 1.0
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dataset:
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name: UD Danish DDT
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split: test
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type: universal_dependencies
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config: da_ddt
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- task:
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name: coreference-resolution
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type: coreference-resolution
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metrics:
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- name: LEA
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type: f_score
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value: 0.4672143289
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dataset:
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name: DaCoref
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type: alexandrainst/dacoref
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split: custom
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- task:
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name: coreference-resolution
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type: coreference-resolution
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metrics:
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- name: Named entity Linking Precision
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type: precision
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value: 0.84
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- name: Named entity Linking Recall
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type: recall
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value: 0.2153846154
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- name: Named entity Linking F Score
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type: f_score
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value: 0.3428571429
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dataset:
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name: DaNED
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type: named-entity-linking
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split: custom
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library_name: spacy
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datasets:
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- universal_dependencies
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- dane
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- alexandrainst/dacoref
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metrics:
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- accuracy
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---
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<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>
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