Sentence Similarity
sentence-transformers
Safetensors
feature-extraction
dense
Generated from Trainer
dataset_size:816
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
Instructions to use ChenyuEcho/corruption_emaillevel_LoRA_newtrainmethod with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ChenyuEcho/corruption_emaillevel_LoRA_newtrainmethod with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ChenyuEcho/corruption_emaillevel_LoRA_newtrainmethod") sentences = [ "Find the detailed cost breakdowns for Raw Materials and Logistics, including supplier invoices, shipping contracts, and related internal correspondence from Q2 and Q3, prepared by Maria Santos for review by end of day Thursday.", "Subject: Resultados de control de calidad – Lote QT-2024-0893\nDate: 2025-12-19T15:12:00\nFrom: Ana Lucia Vega\nParticipants: Javier Moreno\n\nBody:\nHola Javier,\n\nTe comparto los resultados del control de calidad del lote QT-2024-0893, que revisé conforme a lo indicado por Carlos. Los análisis de laboratorio muestran que el contenido de alcohol es de 40.2%, el pH está en 7.1 y las notas de cata confirman un perfil limpio, equilibrado y sin defectos. Todos los parámetros se encuentran dentro de los rangos normales establecidos. Rick ya autorizó el procesamiento final. Si necesitas detalles adicionales o hay algo específico que deba revisar, por favor avísame.\n\nQuedo atenta a tus comentarios.\n\nSaludos,\nAna Lucia\n\n--\nAna Lucia Vega\nAccounts Payable\nASI Mexico", "Subject: Chemical Spill Incident – Immediate Actions and Next Steps\nDate: 2026-01-05T16:48:00\nFrom: Diego Ramirez\nParticipants: Roberto Garza\n\nBody:\nHi Roberto,\n\nWanted to give you a heads up about the minor chemical spill that occurred yesterday afternoon near the maintenance storage area. We contained the spill within 30 minutes using absorbent pads and neutralizing agents. All contaminated PPE and materials were isolated per the protocol. Ricardo has already coordinated sampling and begun the required reporting. We're making improvements to the storage procedures and retraining the crew to prevent recurrence.\n\nI know the EHS permit is critical and we're on top of the paperwork so there won't be any impact on operations. Let me know if you want more details or need me to loop you in on the next steps with Rick.\n\nThanks,\nDiego\n\n--\nDiego Ramirez\nMaintenance Supervisor\nDestilería Agave Spirits", "Subject: Re: Request for Detailed Documentation: Product Cost Analysis\nDate: 2025-11-25T09:20:00\nFrom: Maria Santos\nParticipants: David Chen\n\nBody:\nHi David,\n\nThank you for your feedback and for clarifying the level of detail required regarding the cost variances. I will assemble the detailed breakdowns for 'Raw Materials' and 'Logistics' line items, including the supplier invoices, shipping contracts, and relevant internal correspondence. My team is working to pull these from both our Q2 and Q3 files to ensure we capture all significant changes. I anticipate having the full documentation ready for your review by end of day Thursday, but will let you know immediately if further clarification is needed on specific entries. Please let me know if you have urgent priorities or would like to discuss interim findings.\n\nBest regards,\nMaria" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
| { | |
| "prompts": { | |
| "query": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery:", | |
| "document": "" | |
| }, | |
| "default_prompt_name": null, | |
| "similarity_fn_name": "cosine", | |
| "model_type": "SentenceTransformer", | |
| "__version__": { | |
| "sentence_transformers": "5.2.3", | |
| "transformers": "5.0.0", | |
| "pytorch": "2.10.0+cu128" | |
| } | |
| } |