base_model: sentence-transformers/all-mpnet-base-v2 library_name: sentence-transformers pipeline_tag: sentence-similarity tags:

• sentence-transformers
• sentence-similarity
• feature-extraction
• generated_from_trainer
• dataset_size:5579240
• loss:CachedMultipleNegativesRankingLoss widget:
• source_sentence: Program Coordinator RN sentences:
  • discuss the medical history of the healthcare user, evidence-based approach in general practice, apply various lifting techniques, establish daily priorities, manage time, demonstrate disciplinary expertise, tolerate sitting for long periods, think critically, provide professional care in nursing, attend meetings, represent union members, nursing science, manage a multidisciplinary team involved in patient care, implement nursing care, customer service, work under supervision in care, keep up-to-date with training subjects, evidence-based nursing care, operate lifting equipment, follow code of ethics for biomedical practices, coordinate care, provide learning support in healthcare
  • provide written content, prepare visual data, design computer network, deliver visual presentation of data, communication, operate relational database management system, ICT communications protocols, document management, use threading techniques, search engines, computer science, analyse network bandwidth requirements, analyse network configuration and performance, develop architectural plans, conduct ICT code review, hardware architectures, computer engineering, video-games functionalities, conduct web searches, use databases, use online tools to collaborate
  • nursing science, administer appointments, administrative tasks in a medical environment, intravenous infusion, plan nursing care, prepare intravenous packs, work with nursing staff, supervise nursing staff, clinical perfusion
• source_sentence: Director of Federal Business Development and Capture Mgmt sentences:
  • develop business plans, strive for company growth, develop personal skills, channel marketing, prepare financial projections, perform market research, identify new business opportunities, market research, maintain relationship with customers, manage government funding, achieve sales targets, build business relationships, expand the network of providers, make decisions, guarantee customer satisfaction, collaborate in the development of marketing strategies, analyse business plans, think analytically, develop revenue generation strategies, health care legislation, align efforts towards business development, assume responsibility, solve problems, deliver business research proposals, identify potential markets for companies
  • operate warehouse materials, goods transported from warehouse facilities, organise social work packages, coordinate orders from various suppliers, warehouse operations, work in assembly line teams, work in a logistics team, footwear materials
  • manufacturing plant equipment, use hand tools, assemble hardware components, use traditional toolbox tools, perform product testing, control panel components, perform pre-assembly quality checks, oversee equipment operation, assemble mechatronic units, arrange equipment repairs, assemble machines, build machines, resolve equipment malfunctions, electromechanics, develop assembly instructions, install hydraulic systems, revise quality control systems documentation, detect product defects, operate hydraulic machinery controls, show an exemplary leading role in an organisation, assemble manufactured pipeline parts, types of pallets, perform office routine activities, conform with production requirements, comply with quality standards related to healthcare practice
• source_sentence: director of production sentences:
  • use customer relationship management software, sales strategies, create project specifications, document project progress, attend trade fairs, building automation, sales department processes, work independently, develop account strategy, build business relationships, facilitate the bidding process, close sales at auction, satisfy technical requirements, results-based management, achieve sales targets, manage sales teams, liaise with specialist contractors for well operations, sales activities, use sales forecasting softwares, guarantee customer satisfaction, integrate building requirements in the architectural design, participate actively in civic life, customer relationship management, implement sales strategies
  • translate strategy into operation, lead the brand strategic planning process, assist in developing marketing campaigns, implement sales strategies, sales promotion techniques, negotiate with employment agencies, perform market research, communicate with customers, develop media strategy, change power distribution systems, beverage products, project management, provide advertisement samples, devise military tactics, use microsoft office, market analysis, manage sales teams, create brand guidelines, brand marketing techniques, use sales forecasting softwares, supervise brand management, analyse packaging requirements, provide written content, hand out product samples, channel marketing
  • use microsoft office, use scripting programming, build team spirit, operate games, production processes, create project specifications, analyse production processes for improvement, manage production enterprise, Agile development, apply basic programming skills, document project progress, supervise game operations, work to develop physical ability to perform at the highest level in sport, fix meetings, office software, enhance production workflow, manage a team, set production KPI, manage commercial risks, work in teams, teamwork principles, address identified risks, meet deadlines, consult with production director
• source_sentence: Nursing Assistant sentences:
  • supervise medical residents, observe healthcare users, provide domestic care, prepare health documentation, position patients undergoing interventions, work with broad variety of personalities, supervise food in healthcare, tend to elderly people, monitor patient's vital signs, transfer patients, show empathy, provide in-home support for disabled individuals, hygiene in a health care setting, supervise housekeeping operations, perform cleaning duties, monitor patient's health condition, provide basic support to patients, work with nursing staff, involve service users and carers in care planning, use electronic health records management system, arrange in-home services for patients, provide nursing care in community settings , work in shifts, supervise nursing staff
  • manage relationships with stakeholders, use microsoft office, maintain records of financial transactions, software components suppliers, tools for software configuration management, attend to detail, keep track of expenses, build business relationships, issue sales invoices, financial department processes, supplier management, process payments, perform records management, manage standard enterprise resource planning system
  • inspect quality of products, apply HACCP, test package, follow verbal instructions, laboratory equipment, assist in the production of laboratory documentation, ensure quality control in packaging, develop food safety programmes, packaging engineering, appropriate packaging of dangerous goods, maintain laboratory equipment, SAP Data Services, calibrate laboratory equipment, analyse packaging requirements, write English
• source_sentence: Branch Manager sentences:
  • support employability of people with disabilities, schedule shifts, issue licences, funding methods, maintain correspondence records, computer equipment, decide on providing funds, tend filing machine, use microsoft office, lift stacks of paper, transport office equipment, tend to guests with special needs, provide written content, foreign affairs policy development, provide charity services, philanthropy, maintain financial records, meet deadlines, manage fundraising activities, assist individuals with disabilities in community activities, report on grants, prepare compliance documents, manage grant applications, tolerate sitting for long periods, follow work schedule
  • cook pastry products, create new recipes, food service operations, assess shelf life of food products, apply requirements concerning manufacturing of food and beverages, food waste monitoring systems, maintain work area cleanliness, comply with food safety and hygiene, coordinate catering, maintain store cleanliness, work according to recipe, health, safety and hygiene legislation, install refrigeration equipment, prepare desserts, measure precise food processing operations, conform with production requirements, work in an organised manner, demand excellence from performers, refrigerants, attend to detail, ensure food quality, manufacture prepared meals
  • teamwork principles, office administration, delegate responsibilities, create banking accounts, manage alarm system, make independent operating decisions, use microsoft office, offer financial services, ensure proper document management, own management skills, use spreadsheets software, manage cash flow, integrate community outreach, manage time, perform multiple tasks at the same time, carry out calculations, assess customer credibility, maintain customer service, team building, digitise documents, promote financial products, communication, assist customers, follow procedures in the event of an alarm, office equipment

SentenceTransformer based on sentence-transformers/all-mpnet-base-v2

This is a sentence-transformers model specifically trained for job title matching and similarity. It's finetuned from sentence-transformers/all-mpnet-base-v2 on a large dataset of job titles and their associated skills/requirements. The model maps job titles and descriptions to a 1024-dimensional dense vector space and can be used for semantic job title matching, job similarity search, and related HR/recruitment tasks.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: sentence-transformers/all-mpnet-base-v2
• Maximum Sequence Length: 64 tokens
• Output Dimensionality: 1024 tokens
• Similarity Function: Cosine Similarity
• Training Dataset: 5.5M+ job title pairs
• Primary Use Case: Job title matching and similarity
• Performance: Achieves 0.6457 MAP on TalentCLEF benchmark

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 64, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Asym(
    (anchor-0): Dense({'in_features': 768, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
    (positive-0): Dense({'in_features': 768, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.activation.Tanh'})
  )
)

Usage

Direct Usage (Sentence Transformers)

First install the required packages:

pip install -U sentence-transformers

Then you can load and use the model with the following code:

import torch
import numpy as np
from tqdm.auto import tqdm
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import batch_to_device, cos_sim

# Load the model
model = SentenceTransformer("TechWolf/JobBERT-v2")

def encode_batch(jobbert_model, texts):
    features = jobbert_model.tokenize(texts)
    features = batch_to_device(features, jobbert_model.device)
    features["text_keys"] = ["anchor"]
    with torch.no_grad():
        out_features = jobbert_model.forward(features)
    return out_features["sentence_embedding"].cpu().numpy()

def encode(jobbert_model, texts, batch_size: int = 8):
    # Sort texts by length and keep track of original indices
    sorted_indices = np.argsort([len(text) for text in texts])
    sorted_texts = [texts[i] for i in sorted_indices]
    
    embeddings = []
    
    # Encode in batches
    for i in tqdm(range(0, len(sorted_texts), batch_size)):
        batch = sorted_texts[i:i+batch_size]
        embeddings.append(encode_batch(jobbert_model, batch))
    
    # Concatenate embeddings and reorder to original indices
    sorted_embeddings = np.concatenate(embeddings)
    original_order = np.argsort(sorted_indices)
    return sorted_embeddings[original_order]

# Example usage
job_titles = [
    'Software Engineer',
    'Senior Software Developer',
    'Product Manager',
    'Data Scientist'
]

# Get embeddings
embeddings = encode(model, job_titles)

# Calculate cosine similarity matrix
similarities = cos_sim(embeddings, embeddings)
print(similarities)

The output will be a similarity matrix where each value represents the cosine similarity between two job titles:

tensor([[1.0000, 0.8723, 0.4821, 0.5447],
        [0.8723, 1.0000, 0.4822, 0.5019],
        [0.4821, 0.4822, 1.0000, 0.4328],
        [0.5447, 0.5019, 0.4328, 1.0000]])

In this example:

• The diagonal values are 1.0000 (perfect similarity with itself)
• 'Software Engineer' and 'Senior Software Developer' have high similarity (0.8723)
• 'Product Manager' and 'Data Scientist' show lower similarity with other roles
• All values range between 0 and 1, where higher values indicate greater similarity

Example Use Cases

1. Job Title Matching: Find similar job titles for standardization or matching
2. Job Search: Match job seekers with relevant positions based on title similarity
3. HR Analytics: Analyze job title patterns and similarities across organizations
4. Talent Management: Identify similar roles for career development and succession planning

Training Details

Training Dataset

generator

• Dataset: 5.5M+ job title pairs
• Format: Anchor job titles paired with related skills/requirements
• Training objective: Learn semantic similarity between job titles and their associated skills
• Loss: CachedMultipleNegativesRankingLoss with cosine similarity

Training Hyperparameters

• Batch Size: 2048
• Learning Rate: 5e-05
• Epochs: 1
• FP16 Training: Enabled
• Optimizer: AdamW

Framework Versions

• Python: 3.9.19
• Sentence Transformers: 3.1.0
• Transformers: 4.44.2
• PyTorch: 2.4.1+cu118
• Accelerate: 0.34.2
• Datasets: 3.0.0
• Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

CachedMultipleNegativesRankingLoss

@misc{gao2021scaling,
    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
    year={2021},
    eprint={2101.06983},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}