Instructions to use amalnuaimi/en_spacy_distilroberta_base_acronym_detection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use amalnuaimi/en_spacy_distilroberta_base_acronym_detection with spaCy:
!pip install https://huggingface.co/amalnuaimi/en_spacy_distilroberta_base_acronym_detection/resolve/main/en_spacy_distilroberta_base_acronym_detection-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_spacy_distilroberta_base_acronym_detection") # Importing as module. import en_spacy_distilroberta_base_acronym_detection nlp = en_spacy_distilroberta_base_acronym_detection.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | en_spacy_distilroberta_base_acronym_detection |
| Version | 0.0.0 |
| spaCy | >=3.5.0,<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 (2 labels for 1 components)
| Component | Labels |
|---|---|
ner |
long, short |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
91.96 |
ENTS_P |
92.53 |
ENTS_R |
91.39 |
TRANSFORMER_LOSS |
131333.95 |
NER_LOSS |
569698.70 |
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Evaluation results
- NER Precisionself-reported0.925
- NER Recallself-reported0.914
- NER F Scoreself-reported0.920