This is a sentence-transformers model finetuned from Qwen/Qwen3-Embedding-0.6B on the med_embed-training-triplets-v1 dataset. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: Qwen/Qwen3-Embedding-0.6B
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Training Dataset:
- med_embed-training-triplets-v1
Language: en
License: apache-2.0

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'Qwen3Model'})
  (1): Pooling({'word_embedding_dimension': 1024, '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})
  (2): Normalize()
)

Evaluation

Metrics

Triplet

Dataset: triplet-eval
Evaluated with TripletEvaluator

Metric	Value
cosine_accuracy	0.9743

Information Retrieval

[!NOTE] The dataset was splitted in 90:10 ratio. Eval set had 23269 samples. Extra 23269 negative samples were added for information retrieval.

Datasets: eval-set-23269kq-46538kd and eval-set-23269kq-23269kd
Evaluated with InformationRetrievalEvaluator

Metric	eval-set-23kq-46kd	eval-set-23kq-23kd
cosine_accuracy@1	0.4359	0.5438
cosine_accuracy@3	0.8113	0.8997
cosine_accuracy@5	0.9103	0.953
cosine_accuracy@10	0.9586	0.9703
cosine_precision@1	0.4359	0.5438
cosine_precision@3	0.2704	0.2999
cosine_precision@5	0.1821	0.1906
cosine_precision@10	0.0959	0.097
cosine_recall@1	0.4359	0.5438
cosine_recall@3	0.8113	0.8997
cosine_recall@5	0.9103	0.953
cosine_recall@10	0.9586	0.9703
cosine_ndcg@10	0.7131	0.7837
cosine_mrr@10	0.632	0.7206
cosine_map@100	0.6331	0.7209

luluw/Qwen3-MedEmbed-0.6B-GGUF

Qwen3-MedEmbed-0.6B