Instructions to use MSLars/de_extr_summ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use MSLars/de_extr_summ with spaCy:
!pip install https://huggingface.co/MSLars/de_extr_summ/resolve/main/de_extr_summ-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("de_extr_summ") # Importing as module. import de_extr_summ nlp = de_extr_summ.load() - Notebooks
- Google Colab
- Kaggle
|
Download README.md from MSLars/de_extr_summ: direct link, hf CLI and curl.
- Browser
- Download file 1.09 kB
-
https://huggingface.co/MSLars/de_extr_summ/resolve/main/README.md
- Command line
-
hf download hf://MSLars/de_extr_summ/README.md
-
curl -L -o README.md https://huggingface.co/MSLars/de_extr_summ/resolve/main/README.md
1.09 kB
metadata
tags:
- spacy
- token-classification
language:
- de
model-index:
- name: de_extr_summ
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9932432432
- name: NER Recall
type: recall
value: 0.9639344262
- name: NER F Score
type: f_score
value: 0.9783693844
| Feature | Description |
|---|---|
| Name | de_extr_summ |
| 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 (2 labels for 1 components)
| Component | Labels |
|---|---|
ner |
Agreement, Klöser |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
97.84 |
ENTS_P |
99.32 |
ENTS_R |
96.39 |
TRANSFORMER_LOSS |
8692.25 |
NER_LOSS |
485216.62 |