Sentence Similarity
sentence-transformers
Safetensors
lfm2
liquid
lfm2.5
edge
feature-extraction
custom_code
Instructions to use LiquidAI/LFM2.5-Embedding-350M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LiquidAI/LFM2.5-Embedding-350M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LiquidAI/LFM2.5-Embedding-350M", trust_remote_code=True) 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
| { | |
| "model_type": "SentenceTransformer", | |
| "__version__": { | |
| "sentence_transformers": "5.1.1", | |
| "transformers": "4.56.2", | |
| "pytorch": "2.9.0+cu128" | |
| }, | |
| "prompts": { | |
| "query": "query: ", | |
| "positive": "document: ", | |
| "negative_0": "document: ", | |
| "negative_1": "document: ", | |
| "negative_2": "document: ", | |
| "negative_3": "document: ", | |
| "negative_4": "document: ", | |
| "negative_5": "document: ", | |
| "negative_6": "document: ", | |
| "document": "document: " | |
| }, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } | |