metadata
tags:
- spacy
- token-classification
language:
- multilingual
license: cc-by-nc-sa-4.0
model-index:
- name: xx_eb_ner
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9316591427
- name: NER Recall
type: recall
value: 0.9186371683
- name: NER F Score
type: f_score
value: 0.9251023326
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()
| Feature | Description |
|---|---|
| Name | xx_eb_ner |
| Version | 0.7.0 |
| spaCy | >=3.8.4,<3.9.0 |
| Default Pipeline | tok2vec, ner |
| Components | tok2vec, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | cc-by-nc-sa-4.0 |
| 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 |
92.51 |
ENTS_P |
93.17 |
ENTS_R |
91.86 |
TOK2VEC_LOSS |
10209876.05 |
NER_LOSS |
1606987.89 |