metadata
tags:
- spacy
- token-classification
- ner
language:
- en
license: mit
model-index:
- name: en_ner_job_postings
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8516398746
- name: NER Recall
type: recall
value: 0.8569711538
- name: NER F Score
type: f_score
value: 0.8542971968
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9734810915
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.9208198801
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.9027174273
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.907098331
library_name: spacy
pipeline_tag: text-classification
Custom spaCy NER Model for "Profession," "Facility," and "Experience" Entities
Overview
This spaCy-based Named Entity Recognition (NER) model has been custom-trained to recognize and classify entities related to "profession," "facility," and "experience." It is designed to enhance your text analysis capabilities by identifying these specific entity types in unstructured text data.
Key Features
Custom-trained for high accuracy in recognizing "profession," "facility," and "experience" entities. Suitable for various NLP tasks, such as information extraction, content categorization, and more. Can be easily integrated into your existing spaCy-based NLP pipelines.
Usage
Installation
You can install the custom spaCy NER model using pip:
pip install https://huggingface.co/DaFull/en_ner_job_postings/resolve/main/en_ner_job_postings-any-py3-none-any.whl
Example Usage
Here's how you can use the model for entity recognition in Python:
import spacy
# Load the custom spaCy NER model
nlp = spacy.load("en_ner_job_postings")
# Process your text
text = "HR Specialist needed at XYZ Corporation, Dallas, TX, with expertise in employee relations and a minimum of 4 years of HR experience."
doc = nlp(text)
# Extract named entities
for ent in doc.ents:
print(f"Entity: {ent.text}, Type: {ent.label_}")
Entity Types
The model recognizes the following entity types:
- PROFESSION: Represents professions or job titles.
- FACILITY: Denotes facilities, buildings, or locations.
- EXPERIENCE: Identifies mentions of work experience, durations, or qualifications.
| Feature | Description |
|---|---|
| Name | en_ner_job_postings |
| Version | 3.6.0 |
| spaCy | >=3.6.0,<3.7.0 |
| Default Pipeline | tok2vec, tagger, parser, attribute_ruler, lemmatizer, ner |
| Components | tok2vec, tagger, parser, senter, attribute_ruler, lemmatizer, ner |
| Vectors | 514157 keys, 514157 unique vectors (300 dimensions) |
| License | MIT |
Label Scheme
View label scheme (116 labels for 3 components)
| Component | Labels |
|---|---|
tagger |
$, '', ,, -LRB-, -RRB-, ., :, ADD, AFX, CC, CD, DT, EX, FW, HYPH, IN, JJ, JJR, JJS, LS, MD, NFP, NN, NNP, NNPS, NNS, PDT, POS, PRP, PRP$, RB, RBR, RBS, RP, SYM, TO, UH, VB, VBD, VBG, VBN, VBP, VBZ, WDT, WP, WP$, WRB, XX, _SP, ```` |
parser |
ROOT, acl, acomp, advcl, advmod, agent, amod, appos, attr, aux, auxpass, case, cc, ccomp, compound, conj, csubj, csubjpass, dative, dep, det, dobj, expl, intj, mark, meta, neg, nmod, npadvmod, nsubj, nsubjpass, nummod, oprd, parataxis, pcomp, pobj, poss, preconj, predet, prep, prt, punct, quantmod, relcl, xcomp |
ner |
CARDINAL, DATE, EVENT, EXPERIENCE, FAC, FACILITY, GPE, LANGUAGE, LAW, LOC, MONEY, NORP, ORDINAL, ORG, PERCENT, PERSON, PRODUCT, PROFESSION, QUANTITY, TIME, WORK_OF_ART |
Accuracy
| Type | Score |
|---|---|
TOKEN_ACC |
99.86 |
TOKEN_P |
99.57 |
TOKEN_R |
99.58 |
TOKEN_F |
99.57 |
TAG_ACC |
97.35 |
SENTS_P |
92.19 |
SENTS_R |
89.27 |
SENTS_F |
90.71 |
DEP_UAS |
92.08 |
DEP_LAS |
90.27 |
ENTS_P |
85.16 |
ENTS_R |
85.70 |
ENTS_F |
85.43 |