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EphAsad
/
DomainEmbedder

Sentence Similarity
Transformers
Safetensors
PEFT
sentence-transformers
English
lora
reinforcement-learning
domain-adaptation
sentence-embeddings
curriculum-learning
multi-task-learning
rag
information-retrieval
cross-domain
Eval Results (legacy)
Model card Files Files and versions
xet
Community

Instructions to use EphAsad/DomainEmbedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use EphAsad/DomainEmbedder with Transformers:

    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("EphAsad/DomainEmbedder", dtype="auto")
  • PEFT

    How to use EphAsad/DomainEmbedder with PEFT:

    Task type is invalid.
  • sentence-transformers

    How to use EphAsad/DomainEmbedder with sentence-transformers:

    from sentence_transformers import SentenceTransformer
    
    model = SentenceTransformer("EphAsad/DomainEmbedder")
    
    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
DomainEmbedder / code_lora
596 kB
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  • 1 contributor
History: 2 commits
EphAsad's picture
EphAsad
Update code_lora/README.md
c6df80d verified 3 months ago
  • README.md
    1.54 kB
    Update code_lora/README.md 3 months ago
  • adapter_config.json
    998 Bytes
    Upload 19 files 3 months ago
  • adapter_model.safetensors
    593 kB
    xet
    Upload 19 files 3 months ago