Resumator v2
Fine-tuned sentence-transformer for resume-to-job matching . Encodes resumes and job descriptions into embeddings for cosine similarity search.
Trained with Matryoshka Representation Learning — supports truncation to 384/256/128 dims at inference with minimal quality loss.
Property Value Base Model all-mpnet-base-v2 (109M params)Native Dimensions 768 Recommended Dimensions 384 (Matryoshka truncation) Max Sequence Length 512 tokens Pooling Mean pooling + L2 normalization Parameters ~109.5M Size ~418 MB Training MatryoshkaLoss + MultipleNegativesRankingLoss on 15,600 LLM-scored resume-job pairs
What's New in v2
Improvement v1 v2 Base model all-MiniLM-L6-v2 (22.7M) all-mpnet-base-v2 (109.5M) Training loss CosineSimilarityLoss MatryoshkaLoss + MNRL + CosineSimilarityLoss Training data 624 pairs 15,600 LLM-scored pairs Spearman (held-out) 0.436 0.796 Ranking accuracy 79.9% 98.0%
Usage
1 from sentence_transformers import SentenceTransformer
2
3 # Load with Matryoshka truncation to 384 dims
4 model = SentenceTransformer ( "shankerram3/resumator" , truncate_dim = 384 )
5
6 candidate = "Name: Jane Doe\nSkills: Python, React, PostgreSQL\nResume: Full-stack engineer with 4 years..."
7 job = "Title: Senior Software Engineer\nCompany: TechCorp\nDescription: Looking for full-stack engineer..."
8
9 embeddings = model . encode ( [ candidate , job ] , normalize_embeddings = True )
10 similarity = float ( embeddings [ 0 ] @ embeddings [ 1 ] ) # cosine similarity
Input Format
Candidate:
Name: {name}
Titles: {title1}, {title2}
Skills: {skill1}, {skill2}, {skill3}
Experience: {years} years
Location: {city}, {state}
Resume: {full_resume_text}Job:
Title: {job_title}
Company: {company_name}
Location: {city}, {state}
Experience: {years} years
Description: {full_job_description}Benchmarks
Held-Out Evaluation (Unseen Data)
Evaluated on 500 resume-job pairs using jobs the model never saw during training , scored by Cerebras gpt-oss-120b as ground truth. Run on AMD Instinct MI300X GPU.
Model Dims Params Spearman Ranking Acc Separation IQR resumator-v2 (384) 384 109.5M 0.796 98.0% 0.217 0.185 all-mpnet-base-v2 768 109.5M 0.511 78.7% 0.124 0.160 wynisco-matcher-v1 384 22.7M 0.436 79.9% 0.103 0.123 all-MiniLM-L6-v2 384 22.7M 0.387 76.7% 0.110 0.140 bge-base-en-v1.5 768 109.5M 0.238 61.6% 0.038 0.088 e5-base-v2 768 109.5M 0.188 55.0% 0.011 0.058
Spearman : Rank correlation with LLM recruiter scores (higher = better ranking)Ranking Acc : % of (good, bad) pairs where the good match scores higherSeparation : Mean cosine sim gap between good matches (≥0.6) and bad matches (≤0.3)IQR : Interquartile range — higher means better discrimination between matches
MTEB Benchmarks
Standard sentence-transformer evaluation on public benchmarks (not domain-specific):
Task v1 v2 (384) Delta STS12 0.724 0.716 -0.008 STS13 0.808 0.833 +0.025 STS14 0.755 0.776 +0.021 STS15 0.851 0.852 +0.001 STS16 0.785 0.793 +0.007 STSBenchmark 0.817 0.828 +0.012 SICK-R 0.775 0.804 +0.028 BIOSSES 0.798 0.812 +0.014 SprintDuplicateQuestions 0.941 0.907 -0.034 TwitterSemEval2015 0.675 0.730 +0.056 AskUbuntuDupQuestions 0.633 0.654 +0.021 SciDocsRR 0.872 0.873 +0.001 StackOverflowDupQuestions 0.507 0.513 +0.007 Average 0.765 0.777 +0.012
v2 improves on 11 of 13 MTEB tasks despite being fine-tuned for a specific domain.
Speed
MI300X GPU:
Model Single Batch of 50 resumator-v2 (384) 6.15 ms 13.3 ms (0.27 ms/item) all-mpnet-base-v2 6.09 ms 13.0 ms (0.26 ms/item) wynisco-matcher-v1 3.48 ms 7.6 ms (0.15 ms/item)
Apple M-series CPU:
Model Single Batch of 50 resumator-v2 (384) 9.6 ms 72 ms (1.4 ms/item) all-mpnet-base-v2 8.2 ms 61 ms (1.2 ms/item) wynisco-matcher-v1 5.7 ms 15 ms (0.3 ms/item)
Score Distribution (2,500 resume-job pairs)
Metric v1 v2 (384) Mean 0.546 0.376 Std Dev 0.098 0.124 IQR 0.133 0.186 Min / Max 0.211 / 0.859 0.025 / 0.714
v2 distribution:
0.0-0.1: 13 ( 0.5%)
0.1-0.2: ######## 149 ( 6.0%)
0.2-0.3: ################################ 590 ( 23.6%)
0.3-0.4: ######################################## 722 ( 28.9%)
0.4-0.5: ############################## 543 ( 21.7%)
0.5-0.6: ###################### 410 ( 16.4%)
0.6-0.7: ### 72 ( 2.9%)
0.7-0.8: 1 ( 0.0%)v2 spreads scores more widely (IQR 0.186 vs 0.133), making it easier to set thresholds and distinguish good matches from mediocre ones.
Matryoshka Dimension Quality
Trained with Matryoshka Representation Learning — quality at different truncation levels:
Dimensions Good Match Score Bad Match Score Delta 768 (full) 0.691 0.228 0.462 384 (recommended) 0.696 0.186 0.510
384-dim truncation actually improves separation — Matryoshka loss concentrates the most discriminative information in the first 384 dimensions.
Training Details
Data
15,600 resume-job pairs scored 0.0–1.0 by Cerebras gpt-oss-120b51 candidates × 300 random jobs each
Scoring criteria: technical skills overlap (50%), role/title alignment (30%), experience level match (20%)
Training Pipeline
Phase 1 : MatryoshkaLoss wrapping MultipleNegativesRankingLoss (5 epochs)
Triplets: anchor (candidate) + positive (score ≥ 0.6) + hard negative (score ≤ 0.3)
2,702 triplets, effective batch size 32 (8 × 4 gradient accumulation)
Matryoshka dims: [768, 384, 256, 128]
Phase 2 : CosineSimilarityLoss refinement (2 epochs)
Mid-range pairs (0.3 < score < 0.6) for calibrating the middle of the score range
2,503 pairs, learning rate 5e-6
Training Logs
Phase 1 — MatryoshkaLoss + MNRL (5 epochs):
Epoch Step Loss 0.12 10 2.942 1.06 90 1.856 2.00 170 1.792 3.06 260 1.757 4.00 340 1.684 4.95 420 1.634
Phase 2 — CosineSimilarityLoss (2 epochs):
Epoch Step Loss 0.13 10 0.044 0.51 40 0.009 1.01 80 0.007 1.91 150 0.006
Hardware
Training: AMD Instinct MI300X (ROCm 6.2), ~3 minutes total
Evaluation: AMD Instinct MI300X + Apple M-series CPU
Framework Versions
Python: 3.12.3
Sentence Transformers: 5.2.3
Transformers: 5.3.0
PyTorch: 2.5.1+rocm6.2
Accelerate: 1.13.0
Datasets: 4.7.0
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False, 'architecture': 'MPNetModel'})
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_mean_tokens': True})
(2): Normalize()
)Why Fine-Tune?
Generic sentence-transformers treat resumes as arbitrary text. Fine-tuning teaches:
Domain vocabulary : "OPT", "H1B", "C2C" are visa types, not random acronymsStructural alignment : Match skills sections to requirements sectionsExperience calibration : "3 years Java" → "mid-level Java developer", not "senior architect"Description reading : v2 matches on actual job description content, not just title keywords
General-purpose models (bge, e5) score 0.19–0.24 Spearman on resume-job matching despite leading MTEB leaderboards. Domain fine-tuning is essential for this task.
Limitations
English only, US tech market bias
512 token limit — key information should appear early in the text
Trained on tech/IT recruiting data — may underperform on non-tech roles
LLM-scored training data inherits any biases from the scoring model
Citation
BibTeX
1 @inproceedings{reimers-2019-sentence-bert,
2 title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
3 author = "Reimers, Nils and Gurevych, Iryna",
4 booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
5 month = "11",
6 year = "2019",
7 publisher = "Association for Computational Linguistics",
8 url = "https://arxiv.org/abs/1908.10084",
9 }
License
Apache 2.0
