sentence-transformers How to use Lauther/measuring-embeddings-v3 with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Lauther/measuring-embeddings-v3")
sentences = [
"How are calibration points linked to equipment?",
"How are flow computers and measurement systems related?\nFlow computers can have multiple systems assigned to them. However, a measurement system can only be assigned to one flow computer.\n\nDatabase terminology:\nIn the database, this relationship is referred to as:\n- Meter streams\n- Meter runs\n- Sections\n\nStorage of the relationship:\nThe relationship between a flow computer and its assigned measurement system is stored in a special table.\n\nUser context:\nWhen a user refers to a \"meter stream,\" they are indicating that they are searching for a measurement system assigned to a specific flow computer.",
"How does a flow computer generate and store reports?\nA flow computer generates daily or hourly reports to provide users with operational data. These reports are stored in the flow computer's memory in an organized format.\n\nReport structure:\n- Each report includes:\n- Date and time of the data recording.\n- Data recorded from flow computers.\n\nData storage in tables:\nThe reports are saved in two tables:\n1. Main table (Index):\n - Stores the date, time, and flow computer identifier.\n2. Detail table:\n - Stores the measured values associated with the report.\n\nConnection to the Modbus table:\nThe flow computer's reports are linked to a Modbus table. This table contains the names corresponding to each value in the reports, making it easier to interpret the data.",
"What is uncertainty?\nUncertainty is a measure of confidence in the precision and reliability of results obtained from equipment or measurement systems. It quantifies the potential error or margin of error in measurements.\n\nTypes of uncertainty:\nThere are two main types of uncertainty:\n1. Uncertainty of magnitudes (variables):\n - Refers to the uncertainty of specific variables, such as temperature or pressure.\n - It is calculated after calibrating a device or obtained from the equipment manufacturer's manual.\n - This uncertainty serves as a starting point for further calculations related to the equipment.\n\n2. Uncertainty of the measurement system:\n - Refers to the uncertainty calculated for the overall flow measurement.\n - It depends on the uncertainties of the individual variables (magnitudes) and represents the combined margin of error for the entire system.\n\nKey points:\n- The uncertainties of magnitudes (variables) are the foundation for calculating the uncertainty of the measurement system. Think of them as the \"building blocks.\"\n- Do not confuse the two types of uncertainty:\n - **Uncertainty of magnitudes/variables**: Specific to individual variables (e.g., temperature, pressure).\n - **Uncertainty of the measurement system**: Specific to the overall flow measurement.\n\nDatabase storage for uncertainties:\nIn the database, uncertainty calculations are stored in two separate tables:\n1. Uncertainty of magnitudes (variables):\n - Stores the uncertainty values for specific variables (e.g., temperature, pressure).\n\n2. Uncertainty of the measurement system:\n - Stores the uncertainty values for the overall flow measurement system.\n\nHow to retrieve uncertainty data:\n- To find the uncertainty of the measurement system, join the measurement systems table with the uncertainty of the measurement system table.\n- To find the uncertainty of a specific variable (magnitude), join the measurement systems table with the uncertainty of magnitudes (variables) table.\n\nImportant note:\nDo not confuse the two types of uncertainty:\n- If the user requests the uncertainty of the measurement system, use the first join (measurement systems table + uncertainty of the measurement system table).\n- If the user requests the uncertainty of a specific variable (magnitude) in a report, use the second join (measurement systems table + uncertainty of magnitudes table)."
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
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