Feature | Description |
---|---|
Name | xx_eb_ner |
Version | 0.2.0 |
spaCy | >=3.6.1,<3.7.0 |
Default Pipeline | tok2vec , ner |
Components | tok2vec , ner |
Vectors | 0 keys, 0 unique vectors (0 dimensions) |
Sources | n/a |
License | mit |
Author | philipp-zettl |
Label Scheme
View label scheme (3 labels for 1 components)
Component | Labels |
---|---|
ner |
COURSE_NAME , JOB_TITLE , LOCATION |
Accuracy
Type | Score |
---|---|
ENTS_F |
99.54 |
ENTS_P |
99.56 |
ENTS_R |
99.52 |
TOK2VEC_LOSS |
35345.94 |
NER_LOSS |
32265.61 |
Usage
Install the model via pip:
pip install https://huggingface.co/philipp-zettl/xx_eb_ner/resolve/main/xx_eb_ner-any-py3-none-any.whl
For specific versions, please use the commits provided in the source repository. Example: version 0.1.0
pip install https://huggingface.co/philipp-zettl/xx_eb_ner/resolve/c8585148cabcfd04feec0745c17b148a48933f45/xx_eb_ner-any-py3-none-any.whl
After installing the model with it's dependencies, you can use it like any other SpaCy model:
# Using spacy.load().
import spacy
nlp = spacy.load("xx_eb_ner")
# Importing as module.
import xx_eb_ner
nlp = xx_eb_ner.load()
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Evaluation results
- NER Precisionself-reported0.995
- NER Recallself-reported0.994
- NER F Scoreself-reported0.994