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:
# pip install -U transformers accelerate # 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
Download modules.json from khulnasoft/NeoAI-Embed: direct link, hf CLI and curl.
- Browser
- Download file 229 Bytes
-
https://huggingface.co/khulnasoft/NeoAI-Embed/resolve/main/modules.json
- Command line
-
hf download hf://khulnasoft/NeoAI-Embed/modules.json
-
curl -L -o modules.json https://huggingface.co/khulnasoft/NeoAI-Embed/resolve/main/modules.json
229 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |