| --- |
| base_model: BAAI/bge-small-en-v1.5 |
| library_name: sentence-transformers |
| pipeline_tag: sentence-similarity |
| language: |
| - en |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - resume-matching |
| - job-matching |
| - onnx |
| --- |
| |
| # mdbr-leaf-mt-resume-grader |
|
|
| A compact BGE-small embedding model fine-tuned to rank job descriptions for a |
| resume. It powers the opt-in, on-device Semantic AI feature in the NUWorks |
| Co-op Grader Chrome extension. |
|
|
| This is the expanded-label release from 2026-08-27. It replaces the previous |
| OpenAI-label candidate after training on a larger NUWorks label set and passing |
| a fresh unseen-resume bias check. |
|
|
| ## Evaluation |
|
|
| All numbers below use production-parity mean-vector resume scoring and the |
| quantized `onnx/model_quantized.onnx` artifact. |
|
|
| | Held-out dataset | Pairs | Spearman | NDCG@10 | Top-1 | |
| |---|---:|---:|---:|---:| |
| | Expanded NUWorks test (2 unseen resumes) | 1,944 | 0.8285 | 0.8444 | 0.0% | |
| | Fresh bias check (24 unseen resumes) | 960 | 0.7684 | 0.8683 | 37.5% | |
| | Hugging Face external test | 820 | 0.6682 | 0.8627 | 73.9% | |
|
|
| NeuralFrame external-test Spearman is 0.6120. On the fresh bias check, this |
| model beat the previous candidate on all 24 individual resumes; median |
| per-resume Spearman was 0.7956 and 23/24 resumes scored at least 0.50. |
| The two-resume NUWorks Top-1 result is too coarsely sampled to be useful; rank |
| correlation and NDCG are the meaningful metrics for that split. |
|
|
| The fresh bias-check resumes were never used for training or model selection. |
| After the locked ranking evaluation, their labels were used to fit the |
| display-only monotonic mapping in `calibration.json`; it does not change raw |
| cosine ranking. The jobs came from the existing NUWorks pool, so this result |
| tests resume generalization rather than a fully new job-description |
| distribution. |
|
|
| ## Training |
|
|
| Training used two stages: |
|
|
| 1. Three CoSENT epochs on 6,804 `gpt-5.6-terra`/`gpt-5.6-luna`-labeled NUWorks |
| resume-job pairs, with checkpoint selection on two resume-disjoint |
| validation groups. |
| 2. One replay epoch on the original external datasets to limit catastrophic |
| forgetting: 6,349 Hugging Face rows weighted 3:1 over 7,630 NeuralFrame |
| rows, producing a 26,677-example replay schedule. |
|
|
| No resume crosses the NUWorks train, validation, or test boundaries. The model |
| uses 384-dimensional normalized embeddings, cosine similarity, and a 256-token |
| window. |
|
|
| ## Files |
|
|
| - `model.safetensors`: Sentence Transformers / PyTorch weights |
| - `onnx/model.onnx`: FP32 ONNX export |
| - `onnx/model_quantized.onnx`: int8 deployment model used by the extension |
| - `onnx/model_quint8_avx2.onnx`: uint8 AVX2-compatible alternative |
| - `calibration.json`: revision-pinned cosine-to-display-score mapping |
|
|
| The browser deployment is approximately 34 MB for the quantized model. Resume |
| and job text are processed locally by the extension and are not sent to this |
| repository. |
|
|
| ## Usage |
|
|
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| model = SentenceTransformer("turtlecap/mdbr-leaf-mt-resume-grader") |
| embeddings = model.encode( |
| ["resume text", "job description"], |
| normalize_embeddings=True, |
| ) |
| score = float(embeddings[0] @ embeddings[1]) |
| ``` |
|
|
| For extension parity, long resumes should be chunked before embedding and the |
| normalized chunk embeddings averaged and normalized again. |
|
|
