Sentence Similarity
sentence-transformers
Safetensors
Transformers
qwen2
feature-extraction
text-embeddings-inference
Instructions to use vec-ai/lychee-embed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use vec-ai/lychee-embed with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("vec-ai/lychee-embed") 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 vec-ai/lychee-embed with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("vec-ai/lychee-embed") model = AutoModel.from_pretrained("vec-ai/lychee-embed") - Notebooks
- Google Colab
- Kaggle
| { | |
| "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 | |
| } | |