Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
dense
Generated from Trainer
dataset_size:525
loss:MultipleNegativesRankingLoss
Eval Results (legacy)
text-embeddings-inference
Instructions to use MistyDragon/bge-small-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use MistyDragon/bge-small-finetuned with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("MistyDragon/bge-small-finetuned") sentences = [ "search_document: 60 ◾ Production and Operations Management Systems\ncan compromise quality. Operations management should try to avoid supporting \nproductivity increases gained in this way; the improvement is temporary, at best. \nOther ways of obtaining lower costs such as the use of cheaper components and raw \nmaterials may lower quality.\nThe CEO had something else in mind. When requesting increased productiv -\nity, the CEO meant using technology and good P/OM methods to improve the \nprocess without lowering quality. The CEO’s call for increased productivity is in \nresponse to competitive strategies.\nDecreasing quality to match lower prices is not a way to keep customers. \nImproved productivity, if it is to translate into greater customer satisfaction and \nloyalty, must come from working smarter, not harder. This means improving pro -\nductivity by means other than asking people to work faster, which usually degrades \nquality.\nThis highlights the strong functional interaction between marketing and P/OM \n(which is emphasized in Chapter 11). The managers of these areas are associates \nworking together to manage the effects of price–demand elasticity on production \ncosts and on meeting quality standards. Price–demand elasticity is another example \nof a crucial relationship between systems partners (marketing and P/OM) required \nfor successful strategic planning.\nElasticity is a rate-of-change measure that expresses the degree to which demand \ngrows or shrinks in response to a price change. A product with high elasticity expe-\nriences large decreases (increases) in demand as price increases (decreases), whereas \na product with low elasticity experiences small decreases (increases) in demand with \nthe same degree of price increases (decreases). Low elasticity, called inelasticity, \nmeans that demand levels are relatively insensitive to price changes. Marketing \nmanagers frequently ask market researchers to study the price elasticity of products \nor services to determine how fast demand falls off as price is increased. Products \nthat have no substitutable alternatives (as perceived by customers) usually have low \nelasticity. Product designers who strive for exceptional qualities and production \nmanagers who demand the highest feasible process qualities are creating barriers to \nsubstitutability (inelastic products).\nPerfect inelasticity—when demand does not change, no matter what the \nprice—is an accurate description of the situation when an industrial customer is \ndependent on one supplier for special materials. Most customers try to get out of \nsuch a constraining situation for obvious reasons.\nElasticity is a complex relationship. The rate of change between price and demand \nis not always smooth and regular. There can be kinks in the line or curve. These \noccur, for example, when an increase in price causes demand to increase, which \nmight happen when price becomes high enough to have “snob appeal,” which opens \na new market. Despite difficulties, it is important to measure elasticity, thereby \nrelating price and volume—which are critical factors for production planning.\nThe elasticity–productivity tie between operations management and marketing \nis attributed to the following:", "search_query: Introduction to Production and Operations Management ◾ 41", "search_query: In the context of Production and Operations Management Systems, what is the primary concern when productivity increases are achieved through compromising quality?", "search_query: ---------------------" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle