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RikoteMaster
/
embedder-granite

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:34441
loss:MultipleNegativesRankingLoss
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use RikoteMaster/embedder-granite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use RikoteMaster/embedder-granite with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("RikoteMaster/embedder-granite")
    
    sentences = [
        "inhibitors as antifungal, and antibacterial agents. and in with sulfonylureas, not combination insulin. During clinical tion in 0.4–0.9%. Adverse an increased rate of infections (upper respiratory and tract), (when Tzd), hypoglycemia (when a dose of insulin agogue lowered hypoglycemia. Linagliptin is the class and appears have properties similar to and tin. It approved for and combination with metformin, pioglitazone. COMBINATION THERAPY—ORAL ANTIDIABETIC & in 2 Diabetes Mellitus Failure to maintain response over the owing a in mass, reduction physical activity, mass, in remains disconcerting problem ment of type Multiple medications may glycemic there a should initiated with biguanide. clinical failure monotherapy, a agent or is be insulin dase inhibitor; sulfonylureas insulin because of adverse safety concerns. Third-line multiple medications, or a injectable intensified insulin",
        "inhibitors such as antiviral, antifungal, and certain antibacterial agents. Saxagliptin is approved as monotherapy and in combination with biguanides, sulfonylureas, and Tzds. It has not been studied in combination with insulin. During clinical trials, mono- and combination therapy with sitagliptin resulted in an HbA 1c reduc- tion in the range of 0.4–0.9%. Adverse effects include an increased rate of infections (upper respiratory tract and urinary tract), headaches, peripheral edema (when combined with a Tzd), hypoglycemia (when combined with a sulfonylurea), and hypersensitivity reactions (urticaria, facial edema). The dose of a concurrently administered insulin secret- agogue or insulin may need to be lowered to prevent hypoglycemia. Linagliptin is the most recently introduced drug in this class and appears to have properties similar to sitagliptin and saxaglip- tin. It is approved for use as monotherapy and in combination with metformin, glimepiride, and pioglitazone. COMBINATION THERAPY—ORAL ANTIDIABETIC AGENTS & INJECTABLE MEDICATION Combination Therapy in Type 2 Diabetes Mellitus Failure to maintain a good response to therapy over the long term owing to a progressive decrease in beta-cell mass, reduction in physical activity, decline in lean body mass, or increase in ectopic fat deposition remains a disconcerting problem in the manage- ment of type 2 diabetes. Multiple medications may be required to achieve glycemic control. Unless there is a contraindication, medical therapy should be initiated with a biguanide. If clinical failure occurs with metformin monotherapy, a second agent or insulin is added. The second-line drug can be an insulin secret- agogue, Tzd, incretin-based therapy, amylin analog, or a glucosi- dase inhibitor; preference is given to sulfonylureas or insulin because of cost, adverse effects, and safety concerns. Third-line therapy can include metformin, multiple other oral medications, or a noninsulin injectable and metformin and intensified insulin",
        "61. Glucocorticoids for gastrointestinal use: See Chapter 62. REFERENCES Alesci S et al: Glucocorticoid-induced osteoporosis: From basic mechanisms to clinical aspects. Neuroimmunomodulation 2005;12:1. Bamberger CM, Schulte HM, Chrousos GP: Molecular determinants of gluco- corticoid receptor function and tissue sensitivity to glucocorticoids. Endocr Rev 1996;17:245. Charmandari E, Kino T: Chrousos syndrome: A seminal report, a phylogenetic enigma and the clinical implications of glucocorticoid signaling changes. Eur J Clin Invest 2010;40:932. Charmandari E, Tsigos C, Chrousos GP: Neuroendocrinology of stress. Ann Rev Physiol 2005;67:259. Chrousos GP: Stress and disorders of the stress system. Nat Endocrinol Rev 2009;5:374. Chrousos GP, Kino T: Glucocorticoid signaling in the cell: Expanding clinical implications to complex human behavioral and somatic disorders. In: Glucocorticoids and mood: Clinical manifestations, risk factors, and molecular mechanisms. Proc NY Acad Sci 2009;1179:153. Elenkov IJ, Chrousos GP: Stress hormones, TH1/TH2 patterns, pro/anti-in- flammatory cytokines and susceptibility to disease. Trends Endocrinol Metab 1999;10:359. Elenkov IJ et al: Cytokine dysregulation, inflammation, and wellbeing. Neuroimmunomodulation 2005;12:255. Franchimont D et al: Glucocorticoids and inflammation revisited: The state of the art. Neuroimmunomodulation 2002–03;10:247. Graber AL et al: Natural history of pituitary-adrenal recovery following long-term suppression with corticosteroids. J Clin Endocrinol Metab 1965;25:11. Hochberg Z, Pacak K, Chrousos GP: Endocrine withdrawal syndromes. Endocrine Rev 2003;24:523. Kalantaridou S, Chrousos GP: Clinical review 148:",
        "safely and effectively combined with 5-FU-, irinotecan-, and oxaliplatin-based chemotherapy in the treatment of metastatic colorectal cancer. Bevacizumab is FDA approved as a first-line treatment for metastatic colorectal cancer in combination with any intravenous fluoropyrimidine-contain- ing regimen and is now also approved in combination with che- motherapy for metastatic non-small lung cancer and breast cancer. One potential advantage of this antibody is that it does not appear to exacerbate the toxicities typically observed with cytotoxic che- motherapy. The main safety concerns associated with bevacizumab include hypertension, an increased incidence of arterial throm- boembolic events (transient ischemic attack, stroke, angina, and myocardial infarction), wound healing complications, gastrointes- tinal perforations, and proteinuria. Sorafenib is a small molecule that inhibits multiple receptor tyrosine kinases (RTKs), especially VEGF-R2 and VEGF-R3, platelet-derived growth factor-β (PDGFR-β), and raf kinase. It was initially approved for advanced renal cell cancer and is also approved for advanced hepatocellular cancer. Sunitinib is similar to sorafenib in that it inhibits multiple RTKs, although the specific types are somewhat different. They include PDGFR-α and PDGFR-β, VEGF-R1, VEGF-R2, VEGF-R3, and c-kit. It is approved for the treatment of advanced renal cell cancer and for the treatment of gastrointestinal stromal tumors (GIST) after disease progression on or with intolerance to imatinib. Pazopanib is a small molecule that inhibits multiple RTKs, espe- cially VEGF-R2 and VEGF-R3, PDGFR-β, and raf kinase. This oral agent is approved for the treatment of advanced renal cell cancer. Sorafenib, sunitinib, and pazopanib are metabolized in the liver by the CYP3A4 system, and elimination is primarily hepatic with excretion in feces. Each of these agents has potential interac-"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
embedder-granite
445 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
RikoteMaster's picture
RikoteMaster
Model save
106bf25 verified about 1 year ago
  • 1_Pooling
    Model save about 1 year ago
  • .gitattributes
    1.57 kB
    Model save about 1 year ago
  • README.md
    68.5 kB
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  • config.json
    638 Bytes
    Model save about 1 year ago
  • config_sentence_transformers.json
    205 Bytes
    Model save about 1 year ago
  • model.safetensors
    428 MB
    xet
    Model save about 1 year ago
  • modules.json
    349 Bytes
    Model save about 1 year ago
  • sentence_bert_config.json
    53 Bytes
    Model save about 1 year ago
  • special_tokens_map.json
    963 Bytes
    Model save about 1 year ago
  • tokenizer.json
    17.1 MB
    xet
    Model save about 1 year ago
  • tokenizer_config.json
    1.2 kB
    Model save about 1 year ago
  • training_args.bin
    6.16 kB
    xet
    Model save about 1 year ago
  • training_metadata.json
    753 Bytes
    Model save about 1 year ago
  • vocab.txt
    232 kB
    Model save about 1 year ago