Instructions to use Priyanka-Balivada/en_Resume_Matching_Keywords with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use Priyanka-Balivada/en_Resume_Matching_Keywords with spaCy:
!pip install https://huggingface.co/Priyanka-Balivada/en_Resume_Matching_Keywords/resolve/main/en_Resume_Matching_Keywords-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("en_Resume_Matching_Keywords") # Importing as module. import en_Resume_Matching_Keywords nlp = en_Resume_Matching_Keywords.load() - Notebooks
- Google Colab
- Kaggle
metadata
tags:
- spacy
- token-classification
- ner
- resume matching
language:
- en
model-index:
- name: en_Resume_Matching_Keywords
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.6585136406
- name: NER Recall
type: recall
value: 0.799086758
- name: NER F Score
type: f_score
value: 0.7220216606
license: mit
| Feature | Description |
|---|---|
| Name | en_Resume_Matching_Keywords |
| Version | 0.0.0 |
| spaCy | >=3.7.4,<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 | Priyanka Balivada |
Label Scheme
View label scheme (10 labels for 1 components)
| Component | Labels |
|---|---|
ner |
CERTIFICATIONS, CGPA, EMAIL, EXPERIENCE, LOCATION, NAME, PHONE, QUALIFICATION, SCORES, SKILLS |
Accuracy
| Type | Score |
|---|---|
ENTS_F |
72.20 |
ENTS_P |
65.85 |
ENTS_R |
79.91 |
TRANSFORMER_LOSS |
12326.97 |
NER_LOSS |
19483.22 |