Instructions to use kenkwon/vi_task_ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use kenkwon/vi_task_ner with spaCy:
!pip install https://huggingface.co/kenkwon/vi_task_ner/resolve/main/vi_task_ner-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("vi_task_ner") # Importing as module. import vi_task_ner nlp = vi_task_ner.load() - Notebooks
- Google Colab
- Kaggle
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Download README.md from kenkwon/vi_task_ner: direct link, hf CLI and curl.
- Browser
- Download file 1.19 kB
-
https://huggingface.co/kenkwon/vi_task_ner/resolve/main/README.md
- Command line
-
hf download hf://kenkwon/vi_task_ner/README.md
-
curl -L -o README.md https://huggingface.co/kenkwon/vi_task_ner/resolve/main/README.md
1.19 kB
metadata
tags:
- spacy
- token-classification
language:
- vi
model-index:
- name: vi_task_ner
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8979591837
- name: NER Recall
type: recall
value: 0.8979591837
- name: NER F Score
type: f_score
value: 0.8979591837
This is ner model for task
| Feature | Description |
|---|---|
| Name | vi_task_ner |
| Version | 0.0.0 |
| spaCy | >=3.7.5,<3.8.0 |
| Default Pipeline | tok2vec, ner |
| Components | tok2vec, ner |
| Vectors | 0 keys, 0 unique vectors (0 dimensions) |
| Sources | n/a |
| License | n/a |
| Author | Chánh Hỷ |
Label Scheme
View label scheme (5 labels for 1 components)
| Component | Labels |
|---|---|
ner |
DATE, LOCATION, PERSON, TASK, TIME |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
89.80 |
ENTS_P |
89.80 |
ENTS_R |
89.80 |
TOK2VEC_LOSS |
19657.96 |
NER_LOSS |
12221.95 |