| 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 |
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Evaluation results
- NER Precisionself-reported0.954
- NER Recallself-reported0.934
- NER F Scoreself-reported0.944
!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()