Instructions to use Livesport/xx_ner_sport_entities_uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use Livesport/xx_ner_sport_entities_uncased with spaCy:
!pip install https://huggingface.co/Livesport/xx_ner_sport_entities_uncased/resolve/main/xx_ner_sport_entities_uncased-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("xx_ner_sport_entities_uncased") # Importing as module. import xx_ner_sport_entities_uncased nlp = xx_ner_sport_entities_uncased.load() - Notebooks
- Google Colab
- Kaggle
metadata
tags:
- spacy
- token-classification
language:
- multilingual
model-index:
- name: xx_ner_sport_entities_uncased
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9535962877
- name: NER Recall
type: recall
value: 0.9340909091
- name: NER F Score
type: f_score
value: 0.9437428243
| Feature | Description |
|---|---|
| Name | xx_ner_sport_entities_uncased |
| Version | 1.10.0 |
| spaCy | >=3.5.4,<3.6.0 |
| Default Pipeline | transformer, ner |
| Components | transformer, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (4 labels for 1 components)
| Component | Labels |
|---|---|
ner |
ALIAS_TEAM, PLAYER, TEAM, TOURNAMENT |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
94.37 |
ENTS_P |
95.36 |
ENTS_R |
93.41 |
TRANSFORMER_LOSS |
45704.83 |
NER_LOSS |
203884.18 |