Sentence Similarity
sentence-transformers
Safetensors
bert
feature-extraction
Generated from Trainer
dataset_size:1554
loss:ContrastiveLoss
text-embeddings-inference
Instructions to use officialthinkingstack/Sentence-Matcher-v0.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use officialthinkingstack/Sentence-Matcher-v0.5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("officialthinkingstack/Sentence-Matcher-v0.5") sentences = [ "Patient reports increased joint pain, swelling, and morning stiffness associated with Rheumatoid Arthritis.\nThe patient describes worsening joint pain in the hands and knees, along with increased swelling and morning stiffness lasting over 60 minutes, over the past month.\nMusculoskeletal: Increased joint pain, swelling, morning stiffness.\nRheumatoid Arthritis, managed with disease-modifying antirheumatic drugs (DMARDs).\nWorsening symptoms in a patient with Rheumatoid Arthritis.", "Chronic Obstructive Pulmonary Disease With (Acute) Exacerbation", "Enteroviral Vesicular Stomatitis With Exanthem", "Unspecified Juvenile Rheumatoid Arthritis Of Unspecified Site" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download scheduler.pt from officialthinkingstack/Sentence-Matcher-v0.5: direct link, hf CLI and curl.
- Browser
- Download file 1.06 kB
-
https://huggingface.co/officialthinkingstack/Sentence-Matcher-v0.5/resolve/main/scheduler.pt
- Command line
-
hf download hf://officialthinkingstack/Sentence-Matcher-v0.5/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/officialthinkingstack/Sentence-Matcher-v0.5/resolve/main/scheduler.pt
1.06 kB
- Xet hash:
- 4b7268570198881ca4354702271e448af21f887a08fcbd2cfe9f6108fd1ffc65
- Size of remote file:
- 1.06 kB
- SHA256:
- 27a1f4a8ff132f06024125d5dca678171d935f9e2017641c33f10c350a9027d2
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