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
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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
| 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 | |
| <details> | |
| <summary>View label scheme (5 labels for 1 components)</summary> | |
| | Component | Labels | | |
| | --- | --- | | |
| | **`ner`** | `GPE`, `LOC`, `ORG`, `OTH`, `PER` | | |
| </details> | |
| ### Accuracy | |
| | Type | Score | | |
| | --- | --- | | |
| | `ENTS_F` | 89.46 | | |
| | `ENTS_P` | 90.67 | | |
| | `ENTS_R` | 88.27 | | |
| | `TRANSFORMER_LOSS` | 91081.74 | | |
| | `NER_LOSS` | 126122.54 | |