Sentence Similarity
sentence-transformers
ONNX
Safetensors
English
bert
feature-extraction
resume-matching
job-matching
text-embeddings-inference
Instructions to use turtlecap/mdbr-leaf-mt-resume-grader with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use turtlecap/mdbr-leaf-mt-resume-grader with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("turtlecap/mdbr-leaf-mt-resume-grader") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "schema_version": 1, | |
| "model_release": "expanded-openai-bge-replay-2026-08-27", | |
| "method": "quantile_piecewise_linear", | |
| "cosine_knots": [ | |
| 0.3639161587, | |
| 0.4276538295, | |
| 0.4782150984, | |
| 0.5056520581, | |
| 0.5533524156, | |
| 0.596550107, | |
| 0.6492618918, | |
| 0.6960515141, | |
| 0.7169344574, | |
| 0.7617661941, | |
| 0.8005968928 | |
| ], | |
| "score_knots": [ | |
| 0.02, | |
| 0.03, | |
| 0.05, | |
| 0.08, | |
| 0.12, | |
| 0.3, | |
| 0.43, | |
| 0.62, | |
| 0.68, | |
| 0.82, | |
| 0.88 | |
| ] | |
| } | |