How to use Lauther/measuring-embeddings-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Lauther/measuring-embeddings-v1") sentences = [ "Identify the column that stores the uncertainty value.", "What is measuring equipment?\nMeasuring equipment refers to the devices that make up a measurement system. Each piece of equipment has:\n- A unique serial number for identification.\n- A technical name, such as transmitter, plate, thermometer, etc.\n\nHow is equipment assigned to a measurement system?\nWhen equipment is assigned to a measurement system, it is given a unique identifier called an \"\"Equipment Tag.\"\"\n- If a piece of equipment has a tag, it is considered in use in a measurement system.\n- If it does not have a tag, it is considered spare or unused\n\nEquipment assignment based on technology:\nThe type of equipment assigned to a measurement system depends on the technology used, for example:\n1. Differential technology (for gas measurement):\n - Static pressure transmitters\n - Differential pressure transmitters\n - Temperature transmitters\n - RTDs (thermometers)\n - Orifice plates\n - Straight stretch\n\n2. Linear technology (for gas measurement):\n - Temperature transmitters\n - RTDs\n - Static pressure transmitters\n - Ultrasonic meters\n\nRelationship between equipment and measurement systems:\n- A measurement system can have multiple pieces of equipment.\n- However, a piece of equipment can only be assigned to one measurement system.\n\nDatabase management:\n- The database includes a special table to manage the list of equipment assigned to measurement systems.\n- When a user refers to an \"\"Equipment Tag\"\", they are searching for operational equipment assigned to a measurement system.\n- If a user is looking for spare or unused equipment, they are searching for equipment not listed in the tagged equipment table.\n- Commonly used when user refers directly to an \"\"Equipment Tag\"", "What is equipment calibration?\nCalibration is a metrological verification process used to ensure the accuracy of measurement equipment. It is performed periodically, based on intervals set by the company or a regulatory body.\n\nPurpose of calibration:\nThe calibration process corrects any deviations in how the equipment measures physical magnitudes (variables). This ensures the equipment provides accurate and reliable data.\n\nCalibration cycles:\nThere are two main calibration cycles:\n1. As-found: Represents the equipment's measurement accuracy before any adjustments are made. This cycle is almost always implemented.\n2. As-left: Represents the equipment's measurement accuracy after adjustments are made. This cycle is used depending on regulatory requirements.\n\nCalibration uncertainty:\n- Uncertainty is included in the results of a calibration.\n- Calibration uncertainty refers to the margin of error in the device's measurements, which also affects the uncertainty of the measured variable or magnitude.", "What kind of data store an equipment?\nEquipments can capture meteorological data, such as pressure, temperature, and volume (magnitudes). This data is essential for users to perform various calculations.\n\nData storage:\n- The measured values are stored in a special table in the database for magnitudes. This table contains the values of the variables captured by the equipments.\n- These values are **direct measurements** from the fluid (e.g., raw pressure, temperature, or volume readings). **They are not calculated values**, such as uncertainty.\n- The values stored in the variable values table are **different** from variable uncertainty values, which are calculated separately and represent the margin of error.\n\nAccessing the data:\n- Users typically access the data by referring to the readings from the measurement system, not directly from the individual equipments.\n- The readings are stored in a \"variable values\" table within the database.\n\nLinking variable names:\nIf the user needs to know the name of a variable, they must link the data to another table that stores information about the types of variables." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
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