Instructions to use rahulpointer/en_pipeline with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use rahulpointer/en_pipeline with spaCy:
!pip install https://huggingface.co/rahulpointer/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
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Download README.md from rahulpointer/en_pipeline: direct link, hf CLI and curl.
- Browser
- Download file 1.09 kB
-
https://huggingface.co/rahulpointer/en_pipeline/resolve/main/README.md
- Command line
-
hf download hf://rahulpointer/en_pipeline/README.md
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curl -L -o README.md https://huggingface.co/rahulpointer/en_pipeline/resolve/main/README.md
1.09 kB
metadata
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_pipeline
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.984375
- name: NER Recall
type: recall
value: 0.9921259843
- name: NER F Score
type: f_score
value: 0.9882352941
| Feature | Description |
|---|---|
| Name | en_pipeline |
| Version | 0.0.0 |
| spaCy | >=3.4.4,<3.5.0 |
| Default Pipeline | tok2vec, ner |
| Components | tok2vec, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | n/a |
Label Scheme
View label scheme (3 labels for 1 components)
| Component | Labels |
|---|---|
ner |
MEDICALCONDITION, MEDICINE, PATHOGEN |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
98.82 |
ENTS_P |
98.44 |
ENTS_R |
99.21 |
TOK2VEC_LOSS |
12068.08 |
NER_LOSS |
27961.10 |