Instructions to use kriskyle/en_pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use kriskyle/en_pipeline with spaCy:
!pip install https://huggingface.co/kriskyle/en_pipeline/resolve/main/en_pipeline-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_pipeline") # Importing as module. import en_pipeline nlp = en_pipeline.load() - Notebooks
- Google Colab
- Kaggle
| Feature | Description |
|---|---|
| Name | en_pipeline |
| Version | 0.0.0 |
| spaCy | >=3.4.2,<3.5.0 |
| Default Pipeline | transformer, ner |
| Components | transformer, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | Kristopher Kyle, Hakyung Sung |
Label Scheme
This model identifies and categorizes Argument Structure Constructions (ASCs). ASC types are marked on the main verb of the ASC.
View label scheme (9 labels for 1 components)
| Component | Labels |
|---|---|
ner |
ATTR, CAUS_MOT, DITRAN, INTRAN_MOT, INTRAN_RES, INTRAN_S, PASSIVE, TRAN_RES, TRAN_S |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
91.65 |
ENTS_P |
91.53 |
ENTS_R |
91.78 |
TRANSFORMER_LOSS |
10943.24 |
NER_LOSS |
18950.33 |
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Evaluation results
- NER Precisionself-reported0.915
- NER Recallself-reported0.918
- NER F Scoreself-reported0.917