Instructions to use Tanor/sr_pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use Tanor/sr_pipeline with spaCy:
!pip install https://huggingface.co/Tanor/sr_pipeline/resolve/main/sr_pipeline-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("sr_pipeline") # Importing as module. import sr_pipeline nlp = sr_pipeline.load() - Notebooks
- Google Colab
- Kaggle
metadata
tags:
- spacy
- token-classification
language:
- sr
model-index:
- name: sr_pipeline
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9334730579
- name: NER Recall
type: recall
value: 0.9340414017
- name: NER F Score
type: f_score
value: 0.9337571433
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9648654551