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
File size: 297 Bytes
b8a4e8d | 1 2 3 4 5 6 7 8 9 10 | {
"word_embedding_dimension": 1536,
"pooling_mode_cls_token": false,
"pooling_mode_mean_tokens": false,
"pooling_mode_max_tokens": false,
"pooling_mode_mean_sqrt_len_tokens": false,
"pooling_mode_weightedmean_tokens": false,
"pooling_mode_lasttoken": true,
"include_prompt": true
} |