SentenceTransformer(
  (0): Transformer({'max_seq_length': 32768, '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()
)

pip install -U sentence-transformers

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
queries = [
    "What is Patricia Vasquez\u0027s availability for a risk mitigation meeting today regarding the OR3 surgical incident?",
]
documents = [
    'Subject: Re: URGENT: Incident Report - OR3 Surgical Case\nDate: 2026-01-28T09:16:00\nFrom: David R. Park\nParticipants: Patricia Vasquez\n\nBody:\nDear Patricia,\n\nThank you for your prompt and thorough notification regarding the OR3 surgical incident. Given the seriousness of the situation, I strongly recommend convening a risk mitigation meeting with the core team as soon as possible today. It is essential that we conduct a careful review of all documentation related to the event and ensure our messaging to the patient, family, and regulatory bodies remains consistent and accurate. Please remind all involved staff to refrain from independent written or verbal statements about the incident; all communications should be directed through my office to preserve attorney-client privilege. Additionally, we must protect peer review privilege wherever it may apply.\n\nLet me know your availability for a meeting, and I will coordinate accordingly. I appreciate your diligence as we navigate this sensitive matter together.\n\nBest regards,\nDavid',
    'Subject: Re: Cultural Competency Training Compliance Concerns\nDate: 2025-10-23T12:42:00\nFrom: Maria C. Gonzalez\nParticipants: Carlos J. Rodriguez\n\nBody:\nHi Carlos,\n\nThank you for your prompt response and for taking immediate action to reinforce the cultural competency training requirements with your team. I appreciate your commitment to ensuring that all staff members complete the training by the outlined deadline. Please keep me updated if you encounter any challenges or need additional support, as maintaining regulatory compliance is a top priority for our department. Your attention to this matter is greatly appreciated.\n\nBest regards,\nMaria',
    'Subject: Fuera de la oficina: Karen M. Phillips\nDate: 2025-09-29T14:19:00\nFrom: Karen M. Phillips\nParticipants: Remitente\n\nBody:\nEstimado/a,\n\nGracias por su correo. Lamentablemente, hoy me encuentro fuera de la oficina debido a una enfermedad imprevista. Mi ausencia se debe a la necesidad de un monitoreo riguroso de mis síntomas y ajustes de medicación, prestando especial atención a posibles interacciones farmacológicas y la dosificación precisa para evitar complicaciones.\n\nSi su consulta es urgente, o requiere revisión inmediata de interacción de medicamentos o recomendaciones sobre dosificación, por favor contacte a mi colega Dr. Samuel Rivera al correo [email protected], quien podrá asistirle y revisar cualquier cuestión relativa a protocolos de seguridad farmacológica en mi ausencia.\n\nAgradezco su comprensión ante la situación. Revisaré y responderé su mensaje a mi regreso tan pronto me sea posible.\n\nAtentamente,\nKaren M. Phillips\n\n--\nKaren Phillips, PharmD | Pharmacy Services',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1024] [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[ 0.7148, -0.0698,  0.0708]], dtype=torch.bfloat16)

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}