--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:525 - loss:MultipleNegativesRankingLoss base_model: BAAI/bge-small-en-v1.5 widget: - source_sentence: "search_document: 60  ◾  Production and Operations Management Systems\n\ can compromise quality. Operations management should try to avoid supporting \n\ productivity 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.\n\ Decreasing quality to match lower prices is not a way to keep customers. \nImproved\ \ productivity, if it is to translate into greater customer satisfaction and \n\ loyalty, 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-\n\ riences 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.\n\ Elasticity 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:" sentences: - '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: ---------------------' - source_sentence: "search_document: Supply Chain Management  ◾  361\nchain participants.\ \ These oscillations are known as bullwhip effect and described in \nthe sections\ \ on bullwhip effect later in the chapter.\nFigures 9.10 and 9.11 show the SOH\ \ and orders placed by the retailer and dis-\ntributor, respectively. It is evident\ \ that large oscillations are costing all participants \na great deal. This is\ \ in spite of the fact that a review of the orders made by both the \nretailer\ \ and the distributor leads to the conclusion that the ordering policies fol -\n\ lowed were sensible.\nFigure 9.12 compares the end SOH results for the retailer\ \ and the distributor. \nThe effect had seemed enormous to the retailer. However,\ \ when the comparison is \nmade with the distributor, the retailer’s swings were\ \ gentle. The effect is going to be \neven worse at the producer’s level.\nIf\ \ the increased demand seems to be sustained over a reasonable period of time,\ \ \nthe producer might invest in more capacity (equipment and people) for what\ \ seems \nWeek Begin SOHS upply Net SOH End SOHO rder quantity Delivery weekDemand\n\ 1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n16\n32\n\ 64\n64\n48\n32\n16\n16\n0\n0\n0\n0\n0\n16\n32\n48\n80\n128\n80\n16\n6\n7\n8\n\ 9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n18*\n23\n24\n25\n*FedEx delivery\n\ *Note that the order for 80 cases made in week 17 is expedited via FedEx at extra\ \ cost to\nbe delivered at the beginning of week 18.\n64\n64\n48\n32\n16\n4\n\ –4\n8\n56\n104\n136\n152\n152\n152\n128\n96\n64\n0\n–16\n–64\n16\n16\n16\n16\n\ 16\n16\n32\n64\n64\n48\n32\n16\n16\n0\n0\n0\n0\n80\n16\n32\n80\n80\n64\n48\n32\n\ 20\n28\n72\n120\n152\n168\n168\n168\n152\n128\n96\n64\n80\n0\n–32\n64\n48\n32\n\ 16\n4\n–4\n8\n56\n104\n136\n152\n152\n152\n128\n96\n64\n0\n–16\n–64\n–64\n–16\n\ –32\n–32\n–32\n–28\n–24\n–20\n–16\n–16\n–16\n–16\n–16\n–16\n–24\n–32\n–32\n–64\n\ –94\n–64\n–32\nFigure 9.9 Supply chain simulation of distributors ordering from\ \ producers \n(manufacturers)." sentences: - 'search_query: In the context of job evaluation, which of the following methods is NOT typically used by HR professionals?' - 'search_query: Supply Chain Management  ◾  361' - 'search_query: In the context of Function 5, which is related to Production and Operations Management Systems, what are the implications of technological changes on product design and the issuance of Engineering Change Orders (EDCs)?' - source_sentence: "search_document: 196  ◾  Production and Operations Management\ \ Systems\ndepleted, an order is placed for the EOQ, and further units are taken\ \ from Bin 1. Each \ntime Bin 2 is emptied, a new order is placed—it is equivalent\ \ to reaching the RP. The \ntwo-bin system is not feasible for many kinds of items.\ \ When applicable, much cleri-\ncal work is eliminated. This two-bin system is\ \ well suited to small items such as nuts, \nbolts, and fasteners. These are items\ \ too small and too numerous to make withdrawal \nentries for each transaction.\ \ The same reasoning applies to recording withdrawals of \nliquids, for which\ \ the two-bin system approach is also ideal. See, for example, the \napplication\ \ of two-bin system concept in effective management of a nursing ward for \nreplenishment\ \ of supplies method (http://www.hec.ca/pages/sylvain.landry/en).\n5.11 Periodic\ \ Review (Fixed Time) Inventory Systems\nPeriodic inventory systems are based\ \ on review of inventory levels at regular fixed \nreview periods. These systems\ \ were more popular than perpetual inventory systems \nbefore inventory information\ \ was digitized and put online. These were ideally suited for \nmanual entries\ \ and when actions should be taken periodically rather than randomly.\nComputers\ \ outmoded periodic manual systems primarily designed to save \nmoney on the clerical\ \ aspects of tracking inventory. However, periodic inventory \nsystems continue\ \ to be used for other reasons. These include requirements of suppli-\ners concerning\ \ the timing for accepting new orders, requirements of shippers about \ntiming\ \ deliveries, meeting the schedules of customers, and fulfilling the need to \n\ combine orders to obtain volumes sufficient for shipment discounts.\nSome organizations\ \ have central warehouses that will only accept orders from \ntheir regional distributors\ \ once in a week. Each region expects deliveries on a dif -\nferent day of the\ \ week. Further, some industries prefer the regularity of the periodic \nmethod,\ \ which can be linked to changeover intervals for production processes as \nwell\ \ as the phases of projects. For example, the stages of buildings must be synchro-\n\ nized with what suppliers deliver.\nPeriodic inventory systems also play a part\ \ in an advanced class of inventory \nmodels (called Ss policies) that combine\ \ the ordering rules of perpetual and peri -\nodic order systems to obtain lower\ \ total costs. These blended methods can be \nencountered in big inventory systems\ \ installations such as the Armed Forces use.\nThe optimal interval for periodic\ \ review, t0, is based on the square root relation-\nship given in the following\ \ equation:\n \nt S\nDH0 = 2 .\nThe equation for t0 can be derived as follows:\n\ \ \nt Q\nDD\nDS\nH\nS\nDH0\n0 12 2== ∗= ." sentences: - 'search_query: In the context of the linear breakeven chart, what does the vertical distance between the fixed cost line and the total cost line represent?' - 'search_query: In the context of the preface, how did the rise of complex and large enterprises influence the development of quality management procedures?' - 'search_query: ---------------------' - source_sentence: "search_document: 82  •  Quality   Management:   Theory   and  \ \ Applicatio n\nsimilar disadvantages to an authoritarian style though, with employees\ \ \nbecoming highly dependent on the leader. If the wrong decisions are made,\ \ \nthen all employees may become dissatisfied with the leader.\nDemocratic\n\ In a democratic style, the manager allows the employees to take part in deci-\n\ sion making; therefore, everything is agreed on by the majority. The com-\nmunication\ \ is extensive in both directions (from subordinates to leaders \nand vice versa).\ \ This style can be particularly useful when complex decisions \nneed to be made\ \ that require a range of specialist skills: for example, when \na new information\ \ and communication technologies (ICT) system needs \nto be put in place and the\ \ upper management of the business is computer \nilliterate. From the overall\ \ business’ point of view, job satisfaction and qual-\nity of work will improve.\ \ However, the decision-making process is severely \nslowed down, and the need\ \ for a consensus may lead to not taking the “best” \ndecision for the business.\ \ It can go against a better choice of action.\nLaissez-Faire\nIn a laissez-faire\ \ leadership style, the leader’s role is peripheral and staff man-\nages their\ \ own areas of the business; the leader therefore evades the duties of \nmanagement,\ \ and uncoordinated delegation occurs. The communication in \nthis style is horizontal,\ \ meaning that it is equal in both directions; however, \nvery little communication\ \ occurs in comparison with other styles. The style \nbrings out the best in highly\ \ professional and creative groups of employees; \nhowever, in many cases it is\ \ not deliberate and is simply a result of poor \nmanagement. This leads to a\ \ lack of staff focus and sense of direction, which \nin turn leads to much dissatisfaction\ \ and a poor company image.\nr ewards b ased u PO n Per FO rmance\nA psychological\ \ reward is a process that reinforces behavior—something \nthat, when offered,\ \ causes a behavior to increase in intensity. Reward is \nan operational concept\ \ for describing the positive value an individual \nascribes to an object, behavioral\ \ act, or internal physical state. Primary \n© 2010 by Taylor and Francis Group,\ \ LLC" sentences: - 'search_query: In the context of forecasting demand for the next year, what is the significance of the ''Average SI'' (Seasonal Index) in the calculation of forecasted demand for each quarter?' - 'search_query: Workload Assessment (Forecasting)   ◾  115' - 'search_query: In the context of leadership styles, which style is characterized by extensive communication in both directions and is particularly useful when complex decisions need to be made that require a range of specialist skills?' - source_sentence: "search_document: 386  ◾  Production and Operations Management\ \ Systems\n10.8 Location Decisions Using the Transportation \nModel\nTransportation\ \ costs are a primary concern for a new start-up company or division. \nThis also\ \ applies to an existing company that intends to relocate. Finally, it should\ \ \nbe common practice to reevaluate the current location of an ongoing business\ \ so \nthat the impact of changing conditions and new opportunities are not overlooked.\ \ \nWhen shipping costs are critical for the location decision, the transportation\ \ model \n(TM) can determine minimum cost or maximum profit solutions that specify\ \ opti-\nmal shipping patterns between many locations.\nTransportation costs include\ \ the combined costs of moving raw materials to \nthe plant and of transporting\ \ finished goods from the plant to one or more ware -\nhouses. It is easier to\ \ explain the TM with the following numerical example than \nwith abstract math\ \ equations. A doll manufacturer has decided to build a fac -\ntory in the center\ \ of the United States. More specifically, Missouri and Ohio are \nidentified\ \ as the potential states. Several sites in the two regions have been identi -\n\ fied. Two cities have been chosen as candidates. These are St Louis, Missouri,\ \ and \nColumbus, Ohio. Real-estate costs are about equal in both. The problem\ \ is to \nselect one of the two cities. The decision will be based on the shipping\ \ (transporta -\ntion) costs.\n10.8.1 Shipping (Transportation or Distribution)\ \ Costs\nThe average cost of shipping (also known as the cost of distribution\ \ or cost of trans-\nportation) the components that the company uses to the Columbus,\ \ Ohio, location \nis $6 per production unit. Shipping costs average only $3 per\ \ unit to St Louis, \nMissouri. In TM terminology, shippers (suppliers, in this\ \ case) are called sources or \norigins. Those receiving shipments (producers,\ \ in this case) are called destinations.\nThe average cost of shipping from the\ \ Columbus, Ohio, location to the \n market—distributor’s warehouse is $2 per\ \ unit. The average cost of shipping from \nSt Louis, Missouri, to the market—distributor’s\ \ warehouse is $4 per unit. The same \nterminology applies. The shipper is the\ \ producer (source or origin) and the receivers \nare the distributors or customers\ \ (destinations). The configuration of origins and \ndestinations are shown in\ \ Figure 10.1.\nTotal transportation costs to and from the Columbus, Ohio, plant\ \ are \n$6 + $2 = $8 per unit; for St Louis, Missouri, they are $3 + $4 = $7.\ \ Other things \nbeing equal, the company should choose St Louis, Missouri. However,\ \ the real \nworld is not as simple as this.\nThe problem becomes more complex\ \ when there are a number of origins com -\npeting for shipments to a number of\ \ destinations. We will illustrate the com -\nplexity of the problem and its solution\ \ using the example of Rukna Auto Parts \nManufacturing Company." sentences: - 'search_query: ---------------------' - 'search_query: What is the primary objective of loading in the production scheduling process?' - 'search_query: In the context of the Transportation Model (TM), what are the primary considerations for a company when deciding on a new location for its operations?' pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy@1 - cosine_accuracy@3 - cosine_accuracy@5 - cosine_accuracy@10 - cosine_precision@1 - cosine_precision@3 - cosine_precision@5 - cosine_precision@10 - cosine_recall@1 - cosine_recall@3 - cosine_recall@5 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@100 model-index: - name: BGE Small English v1.5 results: - task: type: information-retrieval name: Information Retrieval dataset: name: dim 384 type: dim_384 metrics: - type: cosine_accuracy@1 value: 0.6893939393939394 name: Cosine Accuracy@1 - type: cosine_accuracy@3 value: 0.803030303030303 name: Cosine Accuracy@3 - type: cosine_accuracy@5 value: 0.8484848484848485 name: Cosine Accuracy@5 - type: cosine_accuracy@10 value: 0.8863636363636364 name: Cosine Accuracy@10 - type: cosine_precision@1 value: 0.6893939393939394 name: Cosine Precision@1 - type: cosine_precision@3 value: 0.2676767676767676 name: Cosine Precision@3 - type: cosine_precision@5 value: 0.16969696969696965 name: Cosine Precision@5 - type: cosine_precision@10 value: 0.08863636363636362 name: Cosine Precision@10 - type: cosine_recall@1 value: 0.6893939393939394 name: Cosine Recall@1 - type: cosine_recall@3 value: 0.803030303030303 name: Cosine Recall@3 - type: cosine_recall@5 value: 0.8484848484848485 name: Cosine Recall@5 - type: cosine_recall@10 value: 0.8863636363636364 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.7854028590069935 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.7530934343434343 name: Cosine Mrr@10 - type: cosine_map@100 value: 0.7569319824286133 name: Cosine Map@100 --- # BGE Small English v1.5 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Base model:** [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 384 dimensions - **Similarity Function:** Cosine Similarity - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True, 'architecture': 'BertModel'}) (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, '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': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SentenceTransformer # Download from the 🤗 Hub model = SentenceTransformer("MistyDragon/bge-small-finetuned") # Run inference sentences = [ 'search_document: 386\u2003 ◾\u2003 Production and Operations Management Systems\n10.8 Location Decisions Using the Transportation \nModel\nTransportation costs are a primary concern for a new start-up company or division. \nThis also applies to an existing company that intends to relocate. Finally, it should \nbe common practice to reevaluate the current location of an ongoing business so \nthat the impact of changing conditions and new opportunities are not overlooked. \nWhen shipping costs are critical for the location decision, the transportation model \n(TM) can determine minimum cost or maximum profit solutions that specify opti-\nmal shipping patterns between many locations.\nTransportation costs include the combined costs of moving raw materials to \nthe plant and of transporting finished goods from the plant to one or more ware -\nhouses. It is easier to explain the TM with the following numerical example than \nwith abstract math equations. A doll manufacturer has decided to build a fac -\ntory in the center of the United States. More specifically, Missouri and Ohio are \nidentified as the potential states. Several sites in the two regions have been identi -\nfied. Two cities have been chosen as candidates. These are St Louis, Missouri, and \nColumbus, Ohio. Real-estate costs are about equal in both. The problem is to \nselect one of the two cities. The decision will be based on the shipping (transporta -\ntion) costs.\n10.8.1 Shipping (Transportation or Distribution) Costs\nThe average cost of shipping (also known as the cost of distribution or cost of trans-\nportation) the components that the company uses to the Columbus, Ohio, location \nis $6 per production unit. Shipping costs average only $3 per unit to St Louis, \nMissouri. In TM terminology, shippers (suppliers, in this case) are called sources or \norigins. Those receiving shipments (producers, in this case) are called destinations.\nThe average cost of shipping from the Columbus, Ohio, location to the \n market—distributor’s warehouse is $2 per unit. The average cost of shipping from \nSt Louis, Missouri, to the market—distributor’s warehouse is $4 per unit. The same \nterminology applies. The shipper is the producer (source or origin) and the receivers \nare the distributors or customers (destinations). The configuration of origins and \ndestinations are shown in Figure 10.1.\nTotal transportation costs to and from the Columbus, Ohio, plant are \n$6 + $2 = $8 per unit; for St Louis, Missouri, they are $3 + $4 = $7. Other things \nbeing equal, the company should choose St Louis, Missouri. However, the real \nworld is not as simple as this.\nThe problem becomes more complex when there are a number of origins com -\npeting for shipments to a number of destinations. We will illustrate the com -\nplexity of the problem and its solution using the example of Rukna Auto Parts \nManufacturing Company.', 'search_query: In the context of the Transportation Model (TM), what are the primary considerations for a company when deciding on a new location for its operations?', 'search_query: What is the primary objective of loading in the production scheduling process?', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities) # tensor([[1.0000, 0.7613, 0.4329], # [0.7613, 1.0000, 0.4239], # [0.4329, 0.4239, 1.0000]]) ``` ## Evaluation ### Metrics #### Information Retrieval * Dataset: `dim_384` * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters: ```json { "truncate_dim": 384 } ``` | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@1 | 0.6894 | | cosine_accuracy@3 | 0.803 | | cosine_accuracy@5 | 0.8485 | | cosine_accuracy@10 | 0.8864 | | cosine_precision@1 | 0.6894 | | cosine_precision@3 | 0.2677 | | cosine_precision@5 | 0.1697 | | cosine_precision@10 | 0.0886 | | cosine_recall@1 | 0.6894 | | cosine_recall@3 | 0.803 | | cosine_recall@5 | 0.8485 | | cosine_recall@10 | 0.8864 | | **cosine_ndcg@10** | **0.7854** | | cosine_mrr@10 | 0.7531 | | cosine_map@100 | 0.7569 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 525 training samples * Columns: positive and anchor * Approximate statistics based on the first 525 samples: | | positive | anchor | |:--------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | | details | | | * Samples: | positive | anchor | |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | search_document: 9192 0.9207 0.9222 0.9236 0.9215 0.9265 0.9279 0.9292 0.9306 0.9319
1.5 0.9332 0.9345 0.9357 0.9370 0.9382 0.9394 0.9406 0.9418 0.9492 0.9441
1.6 0.9452 0.9463 0.9474 0.9484 0.9495 0.9505 0.9515 0.9525 0.9535 0.9545
1.7 0.9554 0.9564 0.9573 0.9582 0.9591 0.9599 0.9608 0.9616 0.9625 0.9633 | search_query: What is the value of the function at x = 1.5? | | search_document: 72  •  Quality   Management:   Theory   and   Applicatio n
secondary school, or gymnasium. Tertiary education normally includes
undergraduate and postgraduate education, as well as vocational educa -
tion and training. Colleges and universities are the main institutions that
provide tertiary education. Tertiary education generally results in the
receipt of certificates, diplomas, or academic degrees.
Higher education includes the teaching, research, and social services
activities of universities, and within the realm of teaching, it includes
both

the undergraduate level (sometimes referred to as tertiary education)
and the graduate (or postgraduate) level (sometimes referred to as gradu-
ate school). Higher education in the United States and Canada generally
involves work toward a degree-level or foundation degree qualification.
In most developed countries, a high proportion of the population (up to
50

percent) now enters higher education at some time in t... | search_query: What is the primary difference between tertiary and higher education as described in the document? | | search_document: 273
Chapter 8
Quality Management
Readers’ Choice—“Quality means doing it
right when no one is looking.”—Henry Ford
Apte, U.M., and Reynolds, C.C., Quality Management at
Kentucky Fried Chicken, Interfaces, 25(3), 1995, p. 6. The pro-
gram developed by Kentucky Fried Chicken (KFC) Corp. to
improve service quality is used as a benchmark for continuous
process improvement by all KFC stores. The reduced service
time as a result of this program is one of the measurements of
quality.
Crosby, P.B., Quality is Free (The Art of Making Quality
Certain). McGraw-Hill, 1979. Crosby (1979) demanded a zero-
defects goal which treats any failures as intolerable.
Harris, C.R., and Yit, W., Successfully Implementing Statistical
Process Control in Integrated Steel Companies, Interfaces, 24(5),
1994, p. 49. Implementation processes of statistical process con-
trol (SPC) projects were analyzed at 12 integrated steel compa-
nies to identify key success (and failure) factors.
Hossein... | search_query: In the context of the document, which company developed a program to improve service quality that is used as a benchmark for continuous process improvement by all KFC stores? | * Loss: [MultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "scale": 20.0, "similarity_fct": "cos_sim" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: epoch - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 8 - `learning_rate`: 2e-05 - `num_train_epochs`: 4 - `lr_scheduler_type`: cosine - `warmup_ratio`: 0.1 - `bf16`: True - `tf32`: False - `load_best_model_at_end`: True - `optim`: adamw_torch_fused - `push_to_hub`: True - `hub_model_id`: MistyDragon/bge-small-finetuned - `push_to_hub_model_id`: bge-small-finetuned - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: epoch - `prediction_loss_only`: True - `per_device_train_batch_size`: 8 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 8 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 4 - `max_steps`: -1 - `lr_scheduler_type`: cosine - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: False - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch_fused - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: True - `resume_from_checkpoint`: None - `hub_model_id`: MistyDragon/bge-small-finetuned - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `hub_revision`: None - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: bge-small-finetuned - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `liger_kernel_config`: None - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | Training Loss | dim_384_cosine_ndcg@10 | |:-------:|:------:|:-------------:|:----------------------:| | -1 | -1 | - | 0.7432 | | 1.0 | 9 | - | 0.7747 | | 1.1212 | 10 | 0.5749 | - | | 2.0 | 18 | - | 0.7759 | | 2.2424 | 20 | 0.3087 | - | | 3.0 | 27 | - | 0.7814 | | 3.3636 | 30 | 0.2328 | - | | **4.0** | **36** | **-** | **0.7854** | * The bold row denotes the saved checkpoint. ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 5.0.0 - Transformers: 4.53.1 - PyTorch: 2.7.1+cu126 - Accelerate: 1.8.1 - Datasets: 3.6.0 - Tokenizers: 0.21.2 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @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", } ``` #### MultipleNegativesRankingLoss ```bibtex @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} } ```