Sentence Similarity
sentence-transformers
Safetensors
Transformers
qwen2
feature-extraction
Qwen2
custom_code
Instructions to use khulnasoft/NeoAI-Embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use khulnasoft/NeoAI-Embed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("khulnasoft/NeoAI-Embed", 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] - Transformers
How to use khulnasoft/NeoAI-Embed with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("khulnasoft/NeoAI-Embed", trust_remote_code=True) model = AutoModel.from_pretrained("khulnasoft/NeoAI-Embed", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "__version__": { | |
| "sentence_transformers": "3.4.1", | |
| "transformers": "4.48.0", | |
| "pytorch": "2.5.0a0+e000cf0ad9.nv24.10" | |
| }, | |
| "prompts": {}, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine" | |
| } |