chest2vec_0.6B
Chest-radiology text embedding model: Qwen/Qwen3-Embedding-0.6B
contrastively LoRA-adapted for chest CT / CXR report retrieval. Embedding = left-padding-aware
last-token (EOS) pooling + L2-norm. Embedding dim: 1024.
Self-contained AutoModel
The LoRA adapter is merged into the weights (model.safetensors) and the tokenizer is bundled,
so loading needs no chest2vec package and no download of the base Qwen3-Embedding weights:
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("chest2vec/chest2vec_0.6B", trust_remote_code=True).eval()
tok = AutoTokenizer.from_pretrained("chest2vec/chest2vec_0.6B", trust_remote_code=True)
docs = ["Bibasilar atelectasis with small bilateral pleural effusions. Cardiomegaly."]
doc_emb = model.embed_texts(docs, tokenizer=tok) # [N, 1024], L2-normalized
# instruction-conditioned query
q_emb = model.embed_instruction_query(
"Retrieve the chest CT report that is similar to the given report.",
["pleural effusion and cardiomegaly"], tokenizer=tok)
vals, idx = model.cosine_topk(q_emb, doc_emb, k=5)
Matryoshka embeddings
Matryoshka (MRL)-trained — truncate to 512 or 256 dims (keep first N dims, re-normalize):
emb512 = model.embed_texts(docs, tokenizer=tok, dim=512)
emb256 = model.embed_texts(docs, tokenizer=tok, dim=256)
Recommended dims: 1024 (full) · 512 · 256 (config.matryoshka_dims). Use the same dim for query and corpus.
Recommended instructions
Instruction-conditioned (Instruct: {instruction}\nQuery: {report}). Apply to the query side;
embed the corpus without an instruction. Trained on chest CT and CXR across these families:
Retrieval — Retrieve the chest CT report that is similar to the given report. ·
Retrieve the CXR report that is similar to the given report. ·
Retrieve the CXR report that is similar to the given report with prior reference omitted.
Summarization — Summarize the following chest CT report · Summarize the following CXR report · Summarize the given report.
Entity extraction (leaf) — Given the following chest CT report, extract the presence/absence of entities · Given the following CXR report, extract the presence/absence of entities
Entity extraction (upper/coarse) — Given the following chest CT report, extract the presence/absence of upper-level entities · Given the following CXR report, extract the presence/absence of upper class entities
Anatomy-specific — From the following chest {CT report | X-ray report}, extract and return only the findings related to {REGION}, ignoring all information about other structures.
- CT regions: lungs · airways and trachea · pleura · mediastinum and hilum · cardiovascular system · chest wall · bones and spine · upper abdomen · lower neck
- CXR regions: lungs and airways · pleura · hila and mediastinum · cardiovascular system · musculoskeletal structures and chest wall · tubes, catheters, and support devices · abdomen
Details
- Base: Qwen/Qwen3-Embedding-0.6B (Apache-2.0) — architecture rebuilt from the bundled config; merged weights loaded from this repo. Default attention
sdpa(useflash_attention_2on Ampere+ for speed). - Merged weights reproduce the original adapter-based embeddings to cosine ≥ 0.999.