Sentence Similarity
Transformers
Safetensors
sentence-transformers
qwen3
text-generation
feature-extraction
embedding
qwen
text-embedding
retrieval
matryoshka
academic-search
scientific-search
multilingual
text-embeddings-inference
Instructions to use LinerAI/Qwen3-Embedding-0.6B-academic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LinerAI/Qwen3-Embedding-0.6B-academic with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("LinerAI/Qwen3-Embedding-0.6B-academic") model = AutoModelForMultimodalLM.from_pretrained("LinerAI/Qwen3-Embedding-0.6B-academic") - sentence-transformers
How to use LinerAI/Qwen3-Embedding-0.6B-academic with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LinerAI/Qwen3-Embedding-0.6B-academic") 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
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