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)
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
- Xet hash:
- 260fbe70e81d1b5005a38b8eb6fa46a3cd8680f63df50cc64a5b2ecd7cef4040
- Size of remote file:
- 86.3 MB
- SHA256:
- 39cc6a82412e1a7b3fbd1c3de5f6fe13541b90783fd47b9203da931f4fd25ada
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