Sentence Similarity
sentence-transformers
Safetensors
Transformers
bidirlm_omni
mteb
embedding
bidirectional
custom_code
Instructions to use BidirLM/BidirLM-Omni-2.5B-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BidirLM/BidirLM-Omni-2.5B-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BidirLM/BidirLM-Omni-2.5B-Embedding", trust_remote_code=True) sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Transformers
How to use BidirLM/BidirLM-Omni-2.5B-Embedding with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BidirLM/BidirLM-Omni-2.5B-Embedding", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Commit ·
ba24293
1
Parent(s): 4d8a7d3
Update ST snippet model name (#2)
Browse files- Update ST snippet model name (89101e45de6eaa6d746208ac750ae1f5c7463c2c)
Co-authored-by: Tom Aarsen <tomaarsen@users.noreply.huggingface.co>
README.md
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@@ -133,7 +133,7 @@ import numpy as np
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import PIL.Image
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("BidirLM/
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# Text queries
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texts = [
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import PIL.Image
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from sentence_transformers import SentenceTransformer
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model = SentenceTransformer("BidirLM/BidirLM-Omni-2.5B-Embedding", trust_remote_code=True)
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# Text queries
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texts = [
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