Instructions to use rame/en_pipeline_ner_model_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use rame/en_pipeline_ner_model_2 with spaCy:
!pip install https://huggingface.co/rame/en_pipeline_ner_model_2/resolve/main/en_pipeline_ner_model_2-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_pipeline_ner_model_2") # Importing as module. import en_pipeline_ner_model_2 nlp = en_pipeline_ner_model_2.load() - Notebooks
- Google Colab
- Kaggle
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Download README.md from rame/en_pipeline_ner_model_2: direct link, hf CLI and curl.
- Browser
- Download file 1.15 kB
-
https://huggingface.co/rame/en_pipeline_ner_model_2/resolve/main/README.md
- Command line
-
hf download hf://rame/en_pipeline_ner_model_2/README.md
-
curl -L -o README.md https://huggingface.co/rame/en_pipeline_ner_model_2/resolve/main/README.md
1.15 kB
metadata
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_pipeline_ner_model_2
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9471397538
- name: NER Recall
type: recall
value: 0.9488574538
- name: NER F Score
type: f_score
value: 0.9479978257
| Feature | Description |
|---|---|
| Name | en_pipeline_ner_model_2 |
| Version | 0.0.0 |
| spaCy | >=3.7.2,<3.8.0 |
| 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 |
allergy_name, cancer, chronic_disease, treatment |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
94.80 |
ENTS_P |
94.71 |
ENTS_R |
94.89 |
TRANSFORMER_LOSS |
406496.45 |
NER_LOSS |
452435.57 |