DaFull/en_ner_job_postings

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 Google, 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_P	75.57
TOKEN_R	60.58
TOKEN_F	67.57
CUSTOM_TAG_ACC	73.35