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base_model: sentence-transformers/all-mpnet-base-v2
library_name: sentence-transformers
pipeline_tag: text-retrieval
license: apache-2.0
tags:
  - sentence-transformers
  - text-retrieval
  - feature-extraction
  - work-domain
  - skill-extraction

ConTeXT-Skill-Extraction-base

This is a sentence-transformers model based on the all-mpnet-base-v2 architecture. It is designed for work-domain AI tasks, specifically skill extraction and normalization, as part of the WorkRB (Work Research Benchmark) framework.

The model is presented in the paper WorkRB: A Community-Driven Evaluation Framework for AI in the Work Domain.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: sentence-transformers/all-mpnet-base-v2
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 dimensions
Similarity Function: Cosine Similarity
License: Apache 2.0

Model Sources

Paper: WorkRB: A Community-Driven Evaluation Framework for AI in the Work Domain
Repository: WorkRB on GitHub
Documentation: Sentence Transformers Documentation

Usage

Direct Usage (Sentence Transformers)

First, install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("jensjorisdecorte/ConTeXT-Skill-Extraction-base")

# Run inference
sentences = [
    'Proficient in Python programming and machine learning.',
    'Experienced in project management and agile methodologies.',
    'Knowledge of cloud computing and AWS infrastructure.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, '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})
)

Training Details

Framework Versions

Python: 3.10.16
Sentence Transformers: 3.4.0
Transformers: 4.48.1
PyTorch: 2.5.1+cpu
Accelerate: 1.3.0
Datasets: 3.2.0
Tokenizers: 0.21.0

Citation

If you find this model useful, please consider citing the following work:

@misc{delange2025unifiedworkembeddings,
      title={Unified Work Embeddings: Contrastive Learning of a Bidirectional Multi-task Ranker}, 
      author={Matthias De Lange and Jens-Joris Decorte and Jeroen Van Hautte},
      year={2025},
      eprint={2511.07969},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2511.07969}, 
}