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
Upload DomainEmbedder-v2.6.zip
Browse files- DomainEmbedder-v2.6.zip +3 -0
DomainEmbedder-v2.6.zip
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