Sentence Similarity
sentence-transformers
Safetensors
Transformers
bidirlm
feature-extraction
mteb
embedding
bidirectional
custom_code
Instructions to use BidirLM/BidirLM-0.6B-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BidirLM/BidirLM-0.6B-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BidirLM/BidirLM-0.6B-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-0.6B-Embedding with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BidirLM/BidirLM-0.6B-Embedding", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "SentenceTransformer", | |
| "__version__": { | |
| "sentence_transformers": "5.2.3", | |
| "transformers": "4.57.6", | |
| "pytorch": "2.6.0" | |
| }, | |
| "prompts": { | |
| "query": "", | |
| "document": "" | |
| }, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } |