Instructions to use rdfez/tl_custom_spacy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use rdfez/tl_custom_spacy with spaCy:
!pip install https://huggingface.co/rdfez/tl_custom_spacy/resolve/main/tl_custom_spacy-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("tl_custom_spacy") # Importing as module. import tl_custom_spacy nlp = tl_custom_spacy.load() - Notebooks
- Google Colab
- Kaggle
| tags: | |
| - spacy | |
| - token-classification | |
| language: | |
| - tl | |
| model-index: | |
| - name: tl_custom_spacy | |
| results: | |
| - task: | |
| name: NER | |
| type: token-classification | |
| metrics: | |
| - name: NER Precision | |
| type: precision | |
| value: 0.951896754 | |
| - name: NER Recall | |
| type: recall | |
| value: 0.9489278752 | |
| - name: NER F Score | |
| type: f_score | |
| value: 0.9504099961 | |
| | Feature | Description | | |
| | --- | --- | | |
| | **Name** | `tl_custom_spacy` | | |
| | **Version** | `0.0.0` | | |
| | **spaCy** | `>=3.8.14,<3.9.0` | | |
| | **Default Pipeline** | `tok2vec`, `ner` | | |
| | **Components** | `tok2vec`, `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 (4 labels for 1 components)</summary> | |
| | Component | Labels | | |
| | --- | --- | | |
| | **`ner`** | `LOC`, `MISC`, `ORG`, `PER` | | |
| </details> | |
| ### Accuracy | |
| | Type | Score | | |
| | --- | --- | | |
| | `ENTS_F` | 95.04 | | |
| | `ENTS_P` | 95.19 | | |
| | `ENTS_R` | 94.89 | | |
| | `TOK2VEC_LOSS` | 46129.63 | | |
| | `NER_LOSS` | 16188.62 | |