Instructions to use rdfez/tl_custom_spacy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use rdfez/tl_custom_spacy with spaCy:
!pip install https://huggingface.co/rdfez/tl_custom_spacy/resolve/main/tl_custom_spacy-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("tl_custom_spacy") # Importing as module. import tl_custom_spacy nlp = tl_custom_spacy.load() - Notebooks
- Google Colab
- Kaggle
metadata
tags:
- spacy
- token-classification
language:
- tl
model-index:
- name: tl_custom_spacy
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.951896754
- name: NER Recall
type: recall
value: 0.9489278752
- name: NER F Score
type: f_score
value: 0.9504099961
| Feature | Description |
|---|---|
| Name | tl_custom_spacy |
| Version | 0.0.0 |
| spaCy | >=3.8.14,<3.9.0 |
| 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 (4 labels for 1 components)
| Component | Labels |
|---|---|
ner |
LOC, MISC, ORG, PER |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
95.04 |
ENTS_P |
95.19 |
ENTS_R |
94.89 |
TOK2VEC_LOSS |
46129.63 |
NER_LOSS |
16188.62 |