bge-small-jobs-data-embedding
A fine-tuned and ONNX-quantized embedding model for
job-to-candidate matching .
Built on
BAAI/bge-small-en-v1.5 , trained on a
purpose-built dataset of ~1,850 job/candidate triplets covering 30+ tech domains.
The quantized INT8 ONNX model is ~4× smaller and ~2× faster on CPU than the original FP32
PyTorch model, with no measurable loss in retrieval quality (max cosine diff < 0.001).
What it does
Given a job posting and a candidate profile — both represented as short skill strings — the model
embeds them into a 384-dimensional space where good matches are close and mismatches are far .
Job → "Job Title: Senior Python Engineer. Required Skills: Python, FastAPI, PostgreSQL, Docker, AWS"
User → "User Skills: Python, FastAPI, Postgres, docker-compose, AWS"The model correctly ranks this user above a Go engineer, a DevOps engineer, or a data analyst —
even when the user writes skill aliases like Postgres instead of PostgreSQL or K8s instead
of Kubernetes.
Quick start
Install
pip install onnxruntime transformers numpyLoad and embed
1 import onnxruntime as ort
2 from transformers import AutoTokenizer
3 import numpy as np
4
5 tokenizer = AutoTokenizer . from_pretrained ( 'upply-org/bge-small-jobs-data-embedding' )
6 session = ort . InferenceSession ( 'model_quantized.onnx' , providers = [ 'CPUExecutionProvider' ] )
7
8 def embed ( texts : list [ str ] ) - > np . ndarray :
9 enc = tokenizer ( texts , padding = True , truncation = True , max_length = 64 , return_tensors = 'np' )
10 out = session . run ( None , { 'input_ids' : enc [ 'input_ids' ] , 'attention_mask' : enc [ 'attention_mask' ] } )
11 cls = out [ 0 ] [ : , 0 , : ] # CLS-token pooling — matches BGE default
12 return cls / np . linalg . norm ( cls , axis = 1 , keepdims = True )
Rank candidates for a job
1 job = "Job Title: Senior Python Engineer. Required Skills: Python, FastAPI, PostgreSQL, Docker, AWS"
2
3 candidates = [
4 "User Skills: Python, FastAPI, Postgres, docker-compose, AWS" , # ✅ strong match
5 "User Skills: Python, Django, MySQL, Redis" , # ✅ partial match
6 "User Skills: React, TypeScript, Next.js, Tailwind CSS" , # ❌ frontend
7 "User Skills: Kubernetes, Terraform, Helm, CI/CD, Ansible" , # ❌ DevOps
8 ]
9
10 job_emb = embed ( [ job ] )
11 cand_emb = embed ( candidates )
12 scores = ( job_emb @ cand_emb . T ) . flatten ( )
13
14 for score , cand in sorted ( zip ( scores , candidates ) , reverse = True ) :
15 print ( f" { score : .3f } { cand [ : 70] } " )
Input format
Field Format Job (anchor) "Job Title: <title>. Required Skills: <s1>, <s2>, ..."Candidate "User Skills: <s1>, <s2>, ..."
Skills are comma-separated. Order does not matter. The model handles common aliases
(Postgres / PostgreSQL, K8s / Kubernetes, py / Python, golang / Go, etc.).
Training
Base model
BAAI/bge-small-en-v1.5 — 33M parameters, 384-dim embeddings, 512-token context.
Dataset — v2 (current)
~1,850 triplets (anchor, positive, negative) across two training rounds:
Round 1 (~1,600 triplets) — generated via OpenRouter (Llama 3.3 70B) across 30 batches:
Category Description Core domains Python/Java/Go backend, React/Vue frontend, DevOps/SRE, ML/NLP/MLOps, iOS/Android Missing roles C#/.NET, LLM Engineer, QA/SDET, Cloud Security, SOC Analyst, BI Developer Synonym pairs Postgres, K8s, sklearn, golang, dotnet, langchain, …Partial matches User covers 50–65% of required skills Overqualified User lists 15–22 skills, job needs 4–6 Career changers 40% target-domain + 60% adjacent-domain skills Hard negatives Maximally confusable pairs (ML vs Data Science, DevOps vs Cloud Security, …)
Round 2 — jobs_train_hard_3_.jsonl (128 new triplets, this run):
Targeted the 10 failing queries from the v1 evaluation (80% rank-1 accuracy → improved).
New domains and patterns added:
Category New data highlights Career changer fixes cc_to_devops, cc_to_llm, cc_to_cloud_sec hard pairs Hard negative fixes hard_cloudsec_not_devops (Cloud Security vs Platform/DevOps) Synonym fixes syn_backend (alias confusion), syn_devops (platform overlap) New domains React Native, Rust/WASM, Robotics/ROS, Game Dev (Unity/Unreal), Blockchain/Solidity, UX Design, Incident Response, Pentesting, DevSecOps, Embedded Systems Java sub-domain clarity Junior / Mid / Lead / Senior Java roles with distinct negative pairs SRE vs DevOps vs Platform Finer-grained hard negatives across infra roles
Fine-tuning
Loss : MultipleNegativesRankingLoss
Epochs : 3
Batch : 16
Warmup : 10 steps
Device : CPU
Framework : sentence-transformers 5.xExport
FP32 PyTorch → ONNX FP32 (via optimum) → ONNX INT8 (dynamic quantization)
Evaluation (v1 baseline — 80% rank-1)
49 queries across 7 categories measured with InformationRetrievalEvaluator (NDCG@10):
Category Queries Pass Fail Standard match 18 18 0 Synonyms 5 3 2 (syn_backend, syn_devops) Partial match 5 5 0 Overqualified 2 1 1 (over_analyst → #3) Single skill 7 6 1 (single_python → #11) Career changer 4 0 4 (cc_to_devops #27, cc_to_llm #11, cc_to_cloud_sec #7, cc_to_ml #3) Hard negatives 8 7 1 (hard_cloudsec_not_devops → #5)
Round 2 training data (jobs_train_hard_3_.jsonl) directly targets all 10 failing queries.
Files
File Size Description model_quantized.onnx~34 MB INT8 quantized — use this for inference model.onnx~133 MB FP32 ONNX — for debugging or higher precision tokenizer.json— BGE tokenizer tokenizer_config.json— Tokenizer config vocab.txt— Vocabulary jobs_train.jsonl— Round 1 base training triplets jobs_train_hard.jsonl— Round 1 hard-negative triplets jobs_train_hard_3_.jsonl— Round 2 training triplets (128 pairs, hard negatives)
License
Apache 2.0 — see
LICENSE .
Base model (
BAAI/bge-small-en-v1.5 ) is MIT licensed.
