Instructions to use MSLars/de_pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use MSLars/de_pipeline with spaCy:
!pip install https://huggingface.co/MSLars/de_pipeline/resolve/main/de_pipeline-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("de_pipeline") # Importing as module. import de_pipeline nlp = de_pipeline.load() - Notebooks
- Google Colab
- Kaggle
|
Download README.md from MSLars/de_pipeline: direct link, hf CLI and curl.
- Browser
- Download file 1.1 kB
-
https://huggingface.co/MSLars/de_pipeline/resolve/main/README.md
- Command line
-
hf download hf://MSLars/de_pipeline/README.md
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curl -L -o README.md https://huggingface.co/MSLars/de_pipeline/resolve/main/README.md
1.1 kB
metadata
tags:
- spacy
- token-classification
language:
- de
model-index:
- name: de_pipeline
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9067489887
- name: NER Recall
type: recall
value: 0.8826943005
- name: NER F Score
type: f_score
value: 0.8945599664
| Feature | Description |
|---|---|
| Name | de_pipeline |
| Version | 0.0.0 |
| spaCy | >=3.7.2,<3.8.0 |
| Default Pipeline | transformer, ner |
| Components | transformer, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (5 labels for 1 components)
| Component | Labels |
|---|---|
ner |
GPE, LOC, ORG, OTH, PER |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
89.46 |
ENTS_P |
90.67 |
ENTS_R |
88.27 |
TRANSFORMER_LOSS |
91081.74 |
NER_LOSS |
126122.54 |