Sentence Similarity
sentence-transformers
Safetensors
Transformers
bidirlm
feature-extraction
mteb
embedding
bidirectional
custom_code
Instructions to use BidirLM/BidirLM-270M-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BidirLM/BidirLM-270M-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BidirLM/BidirLM-270M-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-270M-Embedding with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BidirLM/BidirLM-270M-Embedding", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from BidirLM/BidirLM-270M-Embedding: direct link, hf CLI and curl.
- Browser
- Download file 4.69 MB
-
https://huggingface.co/BidirLM/BidirLM-270M-Embedding/resolve/main/tokenizer.model
- Command line
-
hf download hf://BidirLM/BidirLM-270M-Embedding/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/BidirLM/BidirLM-270M-Embedding/resolve/main/tokenizer.model
4.69 MB
- Xet hash:
- f6aa6b6d77b66c0b5905517df9e5261c32195192839dd05e71e37d6b89933074
- Size of remote file:
- 4.69 MB
- SHA256:
- 1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
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