Instructions to use DanielHellebust/cycLingoNER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use DanielHellebust/cycLingoNER with spaCy:
!pip install https://huggingface.co/DanielHellebust/cycLingoNER/resolve/main/cycLingoNER-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("cycLingoNER") # Importing as module. import cycLingoNER nlp = cycLingoNER.load() - Notebooks
- Google Colab
- Kaggle
File size: 344 Bytes
a1a43a0 61897c0 3ec084e cdcd367 a1a43a0 61897c0 3ec084e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ---
license: mit
language:
- en
library_name: spacy
tags:
- customNER
- bicycle
- ner
pipeline_tag: token-classification
---
# Model Card cycLingoNER model
<!-- Provide a quick summary of what the model is/does. -->
A custom NER model trained on custom dataset to detect bicycle specific terminology as entity. Trained on a blank spaCy model. |