Sentence Similarity
sentence-transformers
Safetensors
gemma3_text
feature-extraction
dense
Generated from Trainer
dataset_size:20000
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Instructions to use yasserrmd/nephrology-gemma-300m-emb with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use yasserrmd/nephrology-gemma-300m-emb with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("yasserrmd/nephrology-gemma-300m-emb") sentences = [ "What is the Dialysis Symptom Index (DSI) and why is it important for assessing symptoms in hemodialysis patients?\n", "Proteinuria in LCDD patients is measured from a 24-hour urine collection. Nephrotic range proteinuria (NRP) is defined as 24-hour proteinuria equal to or greater than 3 grams. This indicates significant protein loss in the urine, which is a characteristic feature of LCDD.", "The Dialysis Symptom Index (DSI) is a self-reported index that assesses the presence and severity of symptoms in patients with end-stage renal disease undergoing hemodialysis. It contains 30 items targeting specific physical and emotional symptoms. The DSI is important for accurately assessing symptoms in hemodialysis patients, both for research and practice purposes, and to improve the care provided to these patients.", "A meta-analysis of trials that utilized high-dose NAC found that it was associated with a lower risk of CIAKI compared to controls. The analysis showed no significant heterogeneity or publication bias. However, the effectiveness of NAC for the prevention of CIAKI is still uncertain, and no definitive conclusions can be drawn at the current time. If a beneficial effect exists, it may be related to the use of higher doses of NAC. Large clinical trials are needed to better define the clinical utility of this agent." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "boi_token": "<start_of_image>", | |
| "bos_token": { | |
| "content": "<bos>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "eoi_token": "<end_of_image>", | |
| "eos_token": { | |
| "content": "<eos>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "image_token": "<image_soft_token>", | |
| "pad_token": { | |
| "content": "<pad>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "unk_token": { | |
| "content": "<unk>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| } | |
| } | |