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Akryl
/
modernbert-embed-base-akryl-matryoshka

Sentence Similarity
sentence-transformers
Safetensors
English
modernbert
feature-extraction
dense
Generated from Trainer
dataset_size:402
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use Akryl/modernbert-embed-base-akryl-matryoshka with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • sentence-transformers

    How to use Akryl/modernbert-embed-base-akryl-matryoshka with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("Akryl/modernbert-embed-base-akryl-matryoshka")
    
    sentences = [
        "<1-hop>\n\nOpinion\nI  have  audited  the  financial  statements  of  the  Ministry  of  Defence  and  Veteran  Affairs (MoDVA),  which  comprise  the  Statement  of  Financial  Position  as  at  30 th June  2023,  the Statement of Financial Performance, Statement of Changes in Equity and Statement of Cash Flows, together with other accompanying statements for the year then ended, and notes to the financial statements, including a summary of significant accounting policies.\nIn my opinion, the accompanying financial statements of the Ministry of Defence and Veteran Affairs for the financial year ended 30 th  June 2023 are prepared, in all material respects, in accordance with Section 51 of the Public Finance Management Act (PFMA), 2015 and the Financial Reporting Guide, 2018 (as amended).",
        "How does the audit process for Kalungu District Local Government and Pader District Local Government follow the Constitution of the Republic of Uganda and what standards are used to ensure compliance with ethical and legal requirements?",
        "What financial statements were audited for MoDVA and KCCA?",
        "How were the water grant funds utilized in the rehabilitation of existing water sources and the drilling of boreholes, and what were the outcomes of these projects?"
    ]
    embeddings = model.encode(sentences)
    
    similarities = model.similarity(embeddings, embeddings)
    print(similarities.shape)
    # [4, 4]
  • Notebooks
  • Google Colab
  • Kaggle
modernbert-embed-base-akryl-matryoshka
600 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
Yeroyan's picture
Yeroyan
Add new SentenceTransformer model
a5b989c verified 11 months ago
  • 1_Pooling
    Add new SentenceTransformer model 11 months ago
  • .gitattributes
    1.52 kB
    initial commit 11 months ago
  • README.md
    36.7 kB
    Add new SentenceTransformer model 11 months ago
  • config.json
    1.21 kB
    Add new SentenceTransformer model 11 months ago
  • config_sentence_transformers.json
    283 Bytes
    Add new SentenceTransformer model 11 months ago
  • model.safetensors
    596 MB
    xet
    Add new SentenceTransformer model 11 months ago
  • modules.json
    349 Bytes
    Add new SentenceTransformer model 11 months ago
  • sentence_bert_config.json
    58 Bytes
    Add new SentenceTransformer model 11 months ago
  • special_tokens_map.json
    694 Bytes
    Add new SentenceTransformer model 11 months ago
  • tokenizer.json
    3.58 MB
    Add new SentenceTransformer model 11 months ago
  • tokenizer_config.json
    20.8 kB
    Add new SentenceTransformer model 11 months ago