Instructions to use bsoviedo/es_COLNER_uncleaned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use bsoviedo/es_COLNER_uncleaned with spaCy:
!pip install https://huggingface.co/bsoviedo/es_COLNER_uncleaned/resolve/main/es_COLNER_uncleaned-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("es_COLNER_uncleaned") # Importing as module. import es_COLNER_uncleaned nlp = es_COLNER_uncleaned.load() - Notebooks
- Google Colab
- Kaggle
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Download README.md from bsoviedo/es_COLNER_uncleaned: direct link, hf CLI and curl.
- Browser
- Download file 1.13 kB
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https://huggingface.co/bsoviedo/es_COLNER_uncleaned/resolve/main/README.md
- Command line
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hf download hf://bsoviedo/es_COLNER_uncleaned/README.md
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curl -L -o README.md https://huggingface.co/bsoviedo/es_COLNER_uncleaned/resolve/main/README.md
1.13 kB
| tags: | |
| - spacy | |
| - token-classification | |
| language: | |
| - es | |
| model-index: | |
| - name: es_COLNER_uncleaned | |
| results: | |
| - task: | |
| name: NER | |
| type: token-classification | |
| metrics: | |
| - name: NER Precision | |
| type: precision | |
| value: 0.960591133 | |
| - name: NER Recall | |
| type: recall | |
| value: 0.975 | |
| - name: NER F Score | |
| type: f_score | |
| value: 0.9677419355 | |
| | Feature | Description | | |
| | --- | --- | | |
| | **Name** | `es_COLNER_uncleaned` | | |
| | **Version** | `0.0.0` | | |
| | **spaCy** | `>=3.8.4,<3.9.0` | | |
| | **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 (1 labels for 1 components)</summary> | |
| | Component | Labels | | |
| | --- | --- | | |
| | **`ner`** | `LOC` | | |
| </details> | |
| ### Accuracy | |
| | Type | Score | | |
| | --- | --- | | |
| | `ENTS_F` | 96.77 | | |
| | `ENTS_P` | 96.06 | | |
| | `ENTS_R` | 97.50 | | |
| | `TRANSFORMER_LOSS` | 2051.59 | | |
| | `NER_LOSS` | 8819.29 | |