Sentence Similarity
sentence-transformers
Safetensors
feature-extraction
dense
Generated from Trainer
dataset_size:36
loss:MultipleNegativesRankingLoss
Instructions to use chaimaJabri/qwen3-coa-embedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use chaimaJabri/qwen3-coa-embedder with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("chaimaJabri/qwen3-coa-embedder") sentences = [ "What chemical is in this certificate of analysis?", "Molecular Weight 73.89", "Product 73154 - Lithium Carbonate pure, 98% - [554-13-2]", "Hydroxylamine Free Base (Electronic Grade aqueous solution)" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| }, | |
| { | |
| "idx": 2, | |
| "name": "2", | |
| "path": "2_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |