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
- Xet hash:
- 51b43105647cb536aaf84da034580fd01dfbba70891510005ae05691d0c24807
- Size of remote file:
- 133 MB
- SHA256:
- fcc33fdac148256cce8479a21270daf800bdeb38862454dcd6d28453c70d7444
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.