prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What is the score achieved by the authors for Track-2?

Read the following text and answer briefly.

Context 1: Paper Info

Title: Two-stage Pipeline for Multilingual Dialect Detection
Publish Date: Unkown
Author List: Ankit Vaidya (from Pune Institute of Computer Technology), Aditya Kane (from Pune Institute of Computer Technology)

Figure

Figure 1: Class distribution of dialects
Figure 2: System diagram for dialect classification The LID classifies the input into one of 3 languages The sample is then further classified into dialects by language specific models 
Figure 3: Confusion matrix of 9-way classification Note that rows are normalized according to the number of samples is that class 
Our complete results for Track-1 using the two-stage dialect detection pipeline Model-* denotes the language of the models used for the experiments 
Performance on Track-1 validation dataset of individual models used in the two-stage pipeline "Lg" stands for language of the model used 
Comparative results of two-way classification using the finetuned (F T ) predictions and predictions adapted from three-way classification models 

abstract

Dialect Identification is a crucial task for localizing various Large Language Models  This paper outlines our approach to the VarDial 2023 DSL-TL shared task  Here we have to identify three or two dialects from three languages each which results in a 9-way classification for Track-1 and 6-way classification for Track-2 respectively 
Our proposed approach consists of a two-stage system and outperforms other participants' systems and previous works in this domain  We achieve a score of 58 54% for Track-1 and 85 61% for Track-2  Our codebase is available publicly 1  

Introduction

Language has been the primary mode of communication for humans since the pre-historic ages  Studies have explored the evolution of language and outlined mathematical models that govern the intricacies of natural language   Inevitably, as humans established civilization in various parts of the world, this language was modified by, and for the group of people occupied by that particular geographical region 
This gave rise to multiple national dialects of the same language  The VarDial workshop (colocated with EACL 2023) explores various dialects and variations of the same language  We participated in the Discriminating Between Similar Languages -True Labels (DSL-TL) shared task  In this task, the participants were provided with data from three languages, with each language having three varieties 
This shared task consisted of two tracks -Track-1 featuring nine-way classification and Track-2 featuring six-way classification  The second track included two particular national dialects of each language (eg  American English and British English), and the first track had one general  We ranked 1 st in both of the tracks 
Moreover, we beat the next best submission by a margin of 4 5% in the first task and 5 6% in the second task We were the only team to surpass the organizer baseline scores  We present our winning solution in this paper  We used an end-to-end deep learning pipeline which consisted of a language identification model and three language-specific models, one for each language 
We converged upon the best combination by doing an elaborate analysis of various models available  Furthermore, in this work we also analyze the performance of the pipeline as a whole and also provide an ablation study  Lastly, we provide some future directions in this area of research 

Related Work

The present literature encompasses various aspects of dialect identification  We study this from three perspectives: large language models, language identification and dialect classification problems 

Large Language Models

The success of transformers and BERT based models was inevitable since the initial boom of the transformer  2017) model  In recent years, many other architectures like RoBERTa and ELECTRA have further pushed the state-of-the-art in this domain  Moreover, autoregressive models like GPT and GPT-2 have also shown their prowess 
Multilingual versions of RoBERTA, namely XLM-RoBERTa are also available  Lastly, language specific models like Spanish BERT (la Rosa y Eduardo G  Ponferrada y Manu Romero y Paulo Villegas y Pablo González de Prado Salas y María Grandury, 2022) and Portuguese BERT are available as well  Our winning solution makes use of these large language models trained on specific languages 

Language Identification Models

Many multilingual language identification models have been developed in order to classify the language of the input sentence beforehand  Even though the initial works used n-gram models and generative mixture models or even conditional random fields and other classical machine learning methods like naive bayes , modern methods have shifted to the use of deep learning for language identification  
Recent works have mainly focused on deep learning based language identification, where handling codemixed data is a big challenge in the domain  For our experiments, we use a version of XLM-RoBERTa finetuned on a language identification dataset 2   This model has near-perfect test accuracy of 99 6% 

Dialect Classification

Dialect classification has been previously solved using statistical methods like Gaussian Mixture Models and Frame Selection Decoding or Support Vector Machines (SVM)   It has been explored relatively sparsely, mostly in the case for local languages   Deep learning approaches have been explored in previous editions of the VarDial workshop shared tasks and otherwise  
Dialect classification was also explored previously as a part of other shared tasks   We want to stress that given the multilingual nature of the dataset, using the present methods directly was not an option  In our work, although we take inspiration from the previous works, we propose a novel system that surpasses the performance of the previous systems by a large margin 

Data

The dataset  We observed that the class PT-BR had the most number of samples (2,724) and the class EN had the least number of samples (349), and thus the imbalance ratio was almost 1:8  We have illustrated the data distribution in Figure   We tried to mitigate this imbalance using over-sampling and weighted sampling methods 
However, the improved data sampling method did not affect the performance 

System Description

This was a problem of multi-class classification having 9 classes for Track-1 and 6 classes for Track-2  The samples were belonging to 3 languages having 3 varieties each, so the classification pipeline was made in 2 stages  The Language Identification (LID) model which is the first stage classifies the sentence into 3 languages: English (EN), Spanish (ES) and Portuguese (PT) 
The LID is a pretrained XLM-RoBERTa that is fine-tuned for the task of language identification  It is able to classify the input sentence into 20 languages  We classify and separate the samples according to their language  The samples corresponding to the specific languages are then fed into the language specific models for dialect identification 
For dialect identification we have used models like BERT and RoBERTa with a linear layer connected to the pooler output of the models  Then fine-tuning is done on the models for dialect identification   using the samples corresponding to the specific languages  For the task of dialect identification we experimented with several pretrained models like XLM-RoBERTa, BERT, ELECTRA, GPT-2 and RoBERTa 
All models were fine-tuned for 20 epochs with a learning rate of 1e-6 and weight decay 1e-6 with a batch size of 8  The best performing model checkpoint was chosen according to the epoch-wise validation macro-F1 score  5 Experiments and Results

Experiments using Large Language Models

For the task of Dialect Identification we have tried various language specific models like XLM-RoBERTa, BERT, ELECTRA, RoBERTa and GPT- 2  The base variant of all these models were used and all the models were used through the Hugging-Face library  The pooler output of these models was passed through a linear layer and the models were fine-tuned 
First, we experimented with different models for Track-1  All the models were trained for 20 epochs with learning rate 1e-6, weight decay 1e-6 and a batch size of 8  We used XLM-RoBERTa as the baseline for all 3 languages  The best performing models for the English language were RoBERTa and BERT whereas GPT-2 was the worst performing 
Similarly the language specific versions of RoBERTa and BERT performed well for the Spanish and Portuguese respectively  Overall the worst performing model was GPT-2 across all 3 languages  The validation F1 scores are present in Table   The two best-performing models for every language were chosen for Track-2 
The same procedure as specified above was used and the F1 scores are present in Table   The train and validation F1 scores for 2-class classification are higher for all models as compared to the F1 score of the same models for 3-class classification  This was mainly due to the poor representation and accuracy of classification of the third class 
We observed symptoms of overfitting in all models after 12-15 epochs and the best validation F1 score was obtained in the range of 4-8 epochs 

LID experiments

The pipeline for dialect identification is divided into two parts as the sentences in the dataset belong to different languages  The stages are described in Section 4  The XLM-RoBERTa we have used for language classification has a test accuracy of 99 6% meaning it correctly classifies all input sentences and hence, can be considered as a perfect classifier 
For the final pipeline we experimented using the two best performing models for each language in Track-1 and Track-2  For both the tracks we experimented with all 8 (2 3 ) possible combinations of models and calculated the validation F1 score for the combined validation dataset which had sentences belonging to all languages 
The validation scores for Track-1 and Track-2 are shown in Table and Table respectively  For both the tracks, the three pipelines with the best validation F1 scores were chosen for submission 

Using 3-way classifier as a 2-way classifier

In Track-1, participants are expected to train a classifier which classifies amongst 9 classes, and in Track-2, participants are expected to train a classifier which classifies amongst 6 classes  These 6 classes are a proper subset of the 9 classes from Track-1  Thus, an intuitive baseline for Track-2 is to use the model finetuned for Track-1, whilst considering only the relevant classes for the latter task 
The classes EN , ES and P T , i e  the classes without any national dialect associated with them are not included in Track-2 as compared to Track-1  Thus, we calculate the predictions for the Track-2 validation dataset using the models for Track-1 and exclude the metrics for Track-1 specific classes to get the metrics for this "adapted" 2-way classification 
We show the results of this experiment in Table and observe that, as expected, the adapted 2-way classification performs worse compared to the explicitly finetuned variant 

Results for Track-1 and Track-2

We now present our experiments and their performance for both tracks  Our experiments for Track-1 are described in Table and our experiments for Track-2 are described in Table   The participants were allowed three submissions for evaluation on the test set, so we submitted predictions using the three systems which performed the best on the validation set 
As mentioned in Section 5 2, we performed 2 3 , i e  a total of 8 experiments using the two best models for each language  We observed that RoBERTa base on English, Spanish BERT base on Spanish and Portuguese BERT base performed the best on the testing set for Track-1  The same combination, with RoBERTa base for English, worked best for Track-2 
All of our submissions were the top submissions for each track, which surpassed the next best competitors by a margin of 4 5% and 5 6% for Track-1 and Track-2 respectively 

Ablation of best submissions

We hereby make some observations of our submissions and other experiments  To assist this, we plot the confusion matrices of our best submissions for Track-1 and Track-2 in Figures respectively  Note that these confusion matrices have their rows (i e  true labels axes) normalized according to the number of samples in the class 
Here are observations from our experiments: 1  BERT-based models outperform other models across all languages: We observe that BERT-based models outperform ELECTRA-based and GPT-2-based models, as shown in Table   We speculate this is because of the inherent architecture of BERT, which combines semantic learning with knowledge retention 
This combination of traits is particularly useful for this task  2  Common labels perform the worst across all languages: We observe that the common labels EN , ES and P T perform the worst, both in the individual as well as the two-stage setup  We hypothesize this is because of the absence of dialect specific words, or words that are specific to the geographical origin of the national dialect (for example, "Yankees" for EN-US and "Oxford" for EN-GB) 
3  English models work better than models of other languages: It can be noted from Figures 4 and 3 that the English models have the best performance across all classes  This can be attributed to two reasons: absence of national dialect specific words and lesser pretraining data in the case of Portuguese 
4  British English is most correctly classified class: We can observe that the Spanish or Portuguese models make equal number of mistakes in the case of either national dialect, in the case of Track-2 (see Figure )  However, in the case of English, the label EN − GB is correctly classified for more than 95% of the cases 
We speculate this is because British English involves slightly distinctive grammar and semantics, which help the model separate it from other classes  5  The proposed 2-step method is scalable for multiple language dialect classification: We can strongly assert that the novel 2-step deep learning method for multilingual dialect classification is a scalable method for the task due to two specific reasons: firstly, the multilingual models (like XLM-RoBERTa) might not have the vocabulary as well as the learning capabilities to learn the minute differences between individual dialects 
Secondly, this system can be quickly expanded for a new language by simply adding a language specific dialect classifier, provided the language identification model supports that particular language 

Conclusion

In this paper we propose a two-stage classification pipeline for dialect identification for multilingual corpora  We conduct thorough ablations on this setup and provide valuable insights  We foresee multiple future directions for this work  The first is to expand this work to many languages and dialects 
Secondly, it is a worthwhile research direction to distill this multi-model setup into a single model with multiple prediction heads  The obvious limitation of this system is the excessive memory consumption due to the usage of language specific models  For low resource languages this system is difficult to train and scale 
We hope that these problems will be addressed by researchers in future works 
Context 2: McPherson County (standard abbreviation: MP) is a county located in the U S  state of Kansas   As of the 2020 census, the county population was 30,223  The largest city and county seat is McPherson  The county is named for Civil War General James B  McPherson 

History

Early history

For many millennia, the Great Plains of North America was inhabited by nomadic Native Americans  From the 16th century to 18th century, the Kingdom of France claimed ownership of large parts of North America  In 1762, after the French and Indian War, France secretly ceded New France to Spain, per the Treaty of Fontainebleau  In 1802, Spain returned most of the land to France, but keeping title to about 7,500 square miles 

In 1803, most of the land for modern day Kansas was acquired by the United States from France as part of the 828,000 square mile Louisiana Purchase for 2 83 cents per acre  In 1848, after the Mexican–American War, the Treaty of Guadalupe Hidalgo with Spain brought into the United States all or part of land for ten future states, including southwest Kansas  In 1854, the Kansas Territory was organized, then in 1861 Kansas became the 34th U S  state 

19th century

From the 1820s to 1870s, the Santa Fe Trail passed through, what is now McPherson County  The trail entered the county, east of Canton, then south of Galva, then north of Inman, and west towards Lyons  In 1855, Charles O  Fuller established a ranch adjacent to the Running Turkey Creek Crossing about two miles south and one mile east of Galva  Fuller's Ranch provided accommodations for travelers on the Santa Fe Trail and was probably the first white settlement in McPherson County 

Peketon County was established in 1860, by the passage of a bill by S  N  Wood:  An act to establish Peketon County  Section 1  - That all that territory west of the sixth principal meridian and south of Township 16, in Kansas Territory, be and the same is hereby erected into a county, to be known by the name of Peketon County  On February 17, 1865, Peketon County was abolished, and McPherson County was made a part of Marion County, which extended from the west line of Chase County to the present western boundary of Kansas 

In 1868, Solomon Stephens and L  N  Holmberg were appointed Justices of the Peace—the first officers in what is now McPherson County  The next year (1869) occurred the first election for the township, now the county of McPherson  McPherson was regularly organized as a county in the spring of 1870, a mass meeting being held at Sweadal  Sweadal, the county seat thus selected, was located about one mile and a half southwest of the present site of Lindsborg  In September, however, the County Commissioners resolved to meet at the latter place, McPherson which had already been located some two years 

In April, 1873, a petition was filed for the county seat re-location  It was signed by 483 voters, and a special election was accordingly ordered for June 10  Upon that day, McPherson received 605 votes, New Gottland 325, King City 3 and Lindsborg 1; McPherson's majority over all, 276  In May the McPherson Town Company had offered, as an inducement for the location of the county seat at this point, the free use of rooms for ten years, and the donation of two squares of land on the town site  The offer was accepted the next month, the County Commissioners selecting blocks 56 and 65  Thus the county seat was established at McPherson and has remained since 

As early as 1875, city leaders of Marion held a meeting to consider a branch railroad from Florence  In 1878, Atchison, Topeka and Santa Fe Railway and parties from Marion County and McPherson County chartered the Marion and McPherson Railway Company   In 1879, a branch line was built from Florence to McPherson, in 1880 it was extended to Lyons, in 1881 it was extended to Ellinwood  The line was leased and operated by the Atchison, Topeka and Santa Fe Railway  The line from Florence to Marion, was abandoned in 1968  In 1992, the line from Marion to McPherson was sold to Central Kansas Railway  In 1993, after heavy flood damage, the line from Marion to McPherson was abandoned  The original branch line connected Florence, Marion, Canada, Hillsboro, Lehigh, Canton, Galva, McPherson, Conway, Windom, Little River, Mitchell, Lyons, Chase, then connected with the original AT&SF main line at Ellinwood 

In 1887, the Chicago, Kansas and Nebraska Railway extended its main line from Herington to Pratt   This main line connected Herington, Ramona, Tampa, Durham, Waldeck, Canton, Galva, McPherson, Groveland, Inman, Medora, Hutchinson, Whiteside, Partridge, Arlington,  Langdon, Turon, Preston, Natrona, Pratt   In 1888, this main line was extended to Liberal   Later, this line was extended to Tucumcari, New Mexico and Santa Rosa, New Mexico, where it made a connection with the Southern Pacific from El Paso, Texas   The Chicago, Kansas and Nebraska Railway was absorbed by the Chicago, Rock Island and Pacific Railway  This line is also called the "Golden State Route" 

20th century
The National Old Trails Road, also known as the Ocean-to-Ocean Highway, was established in 1912, and was routed through Windom, Conway, McPherson 

Geography

According to the U S  Census Bureau, the county has a total area of , of which  is land and  (0 3%) is water 

Adjacent counties
 Saline County (north)
 Dickinson County (northeast)
 Marion County (east)
 Harvey County (southeast)
 Reno County (southwest)
 Rice County (west)
 Ellsworth County (northwest)

Major highways
  Interstate 135
  U S  Route 56
  U S  Route 81
  K-4
  K-61
  K-153

Demographics

The McPherson Micropolitan Statistical Area includes all of McPherson County 

2000 census
As of the census of 2000, there were 29,554 people, 11,205 households, and 7,966 families residing in the county   The population density was 33 people per square mile (13/km2)   There were 11,830 housing units at an average density of 13 per square mile (5/km2)   The racial makeup of the county was 96 53% White, 0 81% Black or African American, 0 34% Native American, 0 32% Asian, 0 06% Pacific Islander, 0 79% from other races, and 1 16% from two or more races   1 94% of the population were Hispanic or Latino of any race  37 1% were of German, 12 9% Swedish, 12 1% American, 6 7% English and 6 3% Irish ancestry according to Census 2000 

There were 11,205 households, out of which 33 00% had children under the age of 18 living with them, 62 50% were married couples living together, 6 00% had a female householder with no husband present, and 28 90% were non-families  25 50% of all households were made up of individuals, and 11 80% had someone living alone who was 65 years of age or older   The average household size was 2 49 and the average family size was 2 99 

In the county, the population was spread out, with 25 40% under the age of 18, 10 30% from 18 to 24, 25 20% from 25 to 44, 21 80% from 45 to 64, and 17 30% who were 65 years of age or older   The median age was 38 years  For every 100 females there were 95 90 males   For every 100 females age 18 and over, there were 92 90 males 

The median income for a household in the county was $41,138, and the median income for a family was $48,243  Males had a median income of $33,530 versus $21,175 for females  The per capita income for the county was $18,921   About 4 20% of families and 6 60% of the population were below the poverty line, including 5 20% of those under age 18 and 8 10% of those age 65 or over 

Government

Presidential elections
McPherson county is often carried by Republican candidates  The last time a Democratic candidate has carried this county was in 1964 by Lyndon B  Johnson 

Laws
Following amendment to the Kansas Constitution in 1986, the county remained a prohibition, or "dry", county until 1996, when voters approved the sale of alcoholic liquor by the individual drink with a 30 percent food sales requirement 

Education

Colleges
 McPherson College in McPherson
 Bethany College in Lindsborg
 Central Christian College in McPherson

Unified school districts
 Smoky Valley USD 400
 McPherson USD 418
 Canton-Galva USD 419
 Moundridge USD 423
 Inman USD 448

School district office in neighboring county
 Goessel USD 411
 Little River-Windom USD 444

Museums
 Birger Sandzén Memorial Gallery in Lindsborg
 McCormick-Deering Days Museum in Inman
 McPherson Museum in McPherson
 Lindsborg Old Mill & Swedish Heritage Museum in Lindsborg
 Kansas Motorcycle Museum in Marquette

Communities

Cities

 Canton
 Galva
 Inman
 Lindsborg
 Marquette
 McPherson (county seat) 
 Moundridge
 Windom

Unincorporated communities
† means a Census-Designated Place (CDP) by the United States Census Bureau 
 Conway
 Elyria†
 Groveland
 Johnstown
 New Gottland
 Roxbury†

Ghost towns
 Alta Mills
 Battle Hill
 Christian
 Doles Park
 Elivon
 King City
 Sweadal

Townships
McPherson County is divided into twenty-five townships   The cities of Lindsborg and McPherson are considered governmentally independent and are excluded from the census figures for the townships   In the following table, the population center is the largest city (or cities) included in that township's population total, if it is of a significant size 

See also
 List of people from McPherson County, Kansas
 National Register of Historic Places listings in McPherson County, Kansas
 McPherson Valley Wetlands
 Maxwell Wildlife Refuge

References

Notes

Further reading

 Wheeler, Wayne Leland  "An Analysis of Social Change in a Swedish-Immigrant Community: The Case of Lindsborg, Kansas " (PhD dissertation, University of Missouri-Columbia; ProQuest Dissertations Publishing, 1959  5905657) 

County
 Through the Years: A Pictorial History of McPherson County; McPherson Sentinel' Heritage House Publishing Co; 1992 
 McPherson County First Courthouse Built About 1869 or 1870; Lindsborg News-Record; March 30, 1959 
 Pioneer Life and Lore of McPherson County, Kansas; Edna Nyquist; Democratic-Opinion Press; 1932 
 A History of the Church of the Brethren in Kansas (includes McPherson College history); Elmer LeRoy Craik; McPherson Daily; Republican Press; 397 pages; 1922 
 Portrait and Biographical Record of Dickinson, Saline, McPherson, and Marion Counties, Kansas; Chapman Bros; 614 pages; 1893 
 Standard Atlas of McPherson County, Kansas; Geo  A  Ogle & Co; 82 pages; 1921 
 Plat Book of McPherson County, Kansas; North West Publishing Co; 50 pages; 1903 
 Edwards' Atlas of McPherson County, Kansas; John P  Edwards; 51 pages; 1884 

Trails
 The Story of the Marking of the Santa Fe Trail by the Daughters of the American Revolution in Kansas and the State of Kansas; Almira Cordry; Crane Co; 164 pages; 1915  (Download 4MB PDF eBook)
 The National Old Trails Road To Southern California, Part 1 (LA to KC); Automobile Club Of Southern California; 64 pages; 1916  (Download 6 8MB PDF eBook)

Mennonite Settlements
 Impact of Mennonite settlement on the cultural landscape of Kansas; Brenda Martin; Kansas State University; 1985/1988  
 Mennonite settlement : the relationship between the physical and cultural environment; Susan Movle; University of Utah; 1975/1886 
 Status of Mennonite women in Kansas in their church and home relationships; Eva Harshbarger; Bluffton College; 1925/1945 

External links

County
 
 McPherson County - Directory of Public Officials
Historical
 , from Hatteberg's People'' on KAKE TV news
Maps
 McPherson County Maps: Current, Historic, KDOT
 Kansas Highway Maps: Current, Historic, KDOT
 Kansas Railroad Maps: Current, 1996, 1915, KDOT and Kansas Historical Society

 
Kansas counties
1867 establishments in Kansas
Populated places established in 1867



ground_truth: 85.61%.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: Who is the county seat of McPherson County?

Read the following text and answer briefly.

Context 4: Sir Simon William English  (born 30 December 1961) is a New Zealand former National Party politician who served as the 39th prime minister of New Zealand from 2016 to 2017  He had previously served as the 17th deputy prime minister of New Zealand and minister of finance from 2008 to 2016 under John Key and the Fifth National Government 

A farmer and public servant before entering politics, English was elected to the New Zealand Parliament in  as the National Party's candidate in the Wallace electorate  He was elevated to Cabinet in 1996 and in 1999 was made minister of finance, although he served for less than a year due to his party's loss at the 1999 general election  In October 2001, English replaced Jenny Shipley as the leader of the National Party (and consequently as Leader of the Opposition)  He led the party to its worst defeat at the 2002 general election, and as a consequence, in October 2003 he was replaced as leader by Don Brash 

In November 2006, after Brash's resignation, English became deputy leader under John Key  After National's victory at the 2008 general election, he became deputy prime minister and was also made minister of finance for the second time  Under English's direction New Zealand's economy maintained steady growth during National's three terms of government  He became a list-only MP after stepping down as an electorate MP at the 2014 general election 

John Key resigned as leader of the National Party and prime minister in December 2016  English won the resulting leadership election unopposed and was sworn in as prime minister on 12 December 2016  His tenure was only ten months, and included a three-month election campaign  In the 2017 general election, National won the largest number of seats but fell short of a majority  The parties holding the balance of power declined to support the existing government, and English was subsequently replaced as prime minister by Jacinda Ardern, leader of the Labour Party  English initially continued on as Leader of the Opposition, but resigned as leader of the National Party on 27 February 2018 and left parliament two weeks later 

Early life
English was born on 30 December 1961 at Lumsden Maternity Centre in Lumsden  He is the eleventh of twelve children of Mervyn English and Norah (née O'Brien) English  His parents purchased Rosedale, a mixed sheep and cropping farm in Dipton, Southland from Mervyn's uncle, Vincent English, a bachelor, in 1944  English was born in the maternity unit at Lumsden 

English attended St Thomas's School in Winton, then boarded at St  Patrick's College in Upper Hutt, where he became head boy  He played in the first XV of the school's rugby team  English went on to study commerce at the University of Otago, where he was a resident at Selwyn College, and then completed an honours degree in English literature at Victoria University of Wellington 

After finishing his studies, English returned to Dipton and farmed for a few years  From 1987 to 1989, he worked in Wellington as a policy analyst for the New Zealand Treasury, at a time when the free market policies favoured by Labour's finance minister Roger Douglas (known collectively as "Rogernomics") were being implemented 

English joined the National Party in 1980, while at Victoria University  He served for a period as chairman of the Southland branch of the Young Nationals, and became a member of the Wallace electorate committee  After moving to Wellington, he served for periods on the Island Bay and Miramar electorate committees, respectively 

Fourth National Government (1990–1999)

At the 1990 general election, English stood as the National candidate in Wallace, replacing the retiring Derek Angus, and was elected with a large majority  He would hold this seat, renamed Clutha-Southland in 1996, until 2014  He and three other newly elected National MPs (Tony Ryall, Nick Smith, and Roger Sowry) were soon identified as rising stars in New Zealand politics, and at various points were dubbed the "brat pack", the "gang of four", and the "young Turks"  In his first term in parliament, English chaired a select committee into social services  He was made a parliamentary under-secretary in 1993, serving under the Minister of Health 

First period in cabinet (1996–1999)
In early 1996, English was elevated to cabinet by Prime Minister Jim Bolger, becoming the Minister for Crown Health Enterprises and Associate Minister of Education (to Wyatt Creech)  He was 34 at the time, becoming the cabinet's youngest member  After the 1996 general election, the National Party was forced into a coalition with New Zealand First to retain government  In the resulting cabinet reshuffle, English emerged as Minister of Health  However, as a condition of the coalition agreement, NZ First's Neil Kirton (a first-term MP) was made Associate Minister of Health, effectively becoming English's deputy  This arrangement was described in the press as a "shotgun marriage", and there were frequent differences of opinion between the two ministers  After their relationship became unworkable, Kirton was sacked from the role in August 1997, with the agreement of NZ First leader Winston Peters 

As Minister of Health, English was responsible for continuing the reforms to the public health system that National had begun after the 1990 general election  The reforms were unpopular, and health was perceived as one of the government's weaknesses, with the health portfolio consequently being viewed as a challenge  English believed that the unpopularity of the reforms was in part due to a failure in messaging, and encouraged his National colleagues to avoid bureaucratic and money-focused language (such as references to "balance sheets" and "user charges") and instead talk about the improvements to services the government's reforms would bring  He also rejected the idea that public hospitals could be run as commercial enterprises, a view which some of his colleagues had previously promoted 

By early 1997, as dissatisfaction with Bolger's leadership began to grow, English was being touted as a potential successor, along with Jenny Shipley and Doug Graham  His age (35) was viewed as the main impediment to a successful leadership run  National's leadership troubles were resolved in December 1997, when Bolger resigned and Shipley was elected to the leadership unopposed  English had been a supporter of Bolger as leader, but Shipley reappointed him Minister of Health in her new cabinet 

English was promoted to Minister of Finance in a reshuffle in January 1999, a position which was at the time subordinate to the Treasurer, Bill Birch  After a few months, the pair switched positions as part of Birch's transition to retirement, with English assuming the senior portfolio  In early interviews, he emphasised his wish to be seen as a pragmatist rather than an ideologue, and said that the initiatives of some of his predecessors (Roger Douglas's "Rogernomics" and Ruth Richardson's "Ruthanasia") had focused on "fruitless, theoretical debates" when "people just want to see problems solved" 

Opposition (1999–2008)

After the National Party lost the 1999 election to Helen Clark's Labour Party, English continued on in the shadow cabinet as National's spokesperson for finance  He was elected deputy leader of the party in February 2001, following the resignation of Wyatt Creech, with Gerry Brownlee being his unsuccessful opponent 

Leader of the Opposition
In October 2001, after months of speculation, Jenny Shipley resigned as leader of the National Party after being told she no longer had the support of the party caucus  English was elected as her replacement unopposed (with Roger Sowry as his deputy), and consequently became Leader of the Opposition  However, he did not openly organise against Shipley, and according to The Southland Times "there was almost an element of 'aw, shucks, I'll do it then' about Mr English's ascension" 

Aged 39 when he was elected, English became the second-youngest leader in the National Party's history, after Jim McLay (who was 38 when elected in 1984)  He also became only the third Southlander to lead a major New Zealand political party, after Joseph Ward and Adam Hamilton  However, English failed to improve the party's performance  In the 2002 election, National suffered its worst electoral defeat ever, gaining barely more than twenty percent of the vote  English described it as "the worst day of my political life"  Both party insiders and the general public were split as to how much to blame him for the loss, but most of the party believed that English would be able to rebuild National's support 

By late 2003, however, National's performance in opinion polls remained poor  The party had briefly increased its popularity in the year following the election, but by October its support had fallen to levels only slightly better than what it achieved in the last ballot  English also appeared in a boxing match for a charity against entertainer Ted Clarke  This did not boost his polling or that of the National party either, with suggestions that it devalued his image as a serious politician  Don Brash, former governor of the Reserve Bank and a relative newcomer to politics, began to build up support to replace English  On 28 October, Brash gained sufficient backing in Caucus to defeat English in a leadership contest 

Shadow cabinet roles and deputy leader
On 2 November 2003, when Brash changed responsibilities for certain MPs, English became National's spokesman for education, ranked at fifth place in the party's parliamentary hierarchy  He remained in parliament after the 2005 election  In his new shadow education portfolio, English performed strongly, and remained a party favourite despite his election defeat as leader in 2002, eventually being returned to the finance portfolio in August 2004 as deputy spokesman (while still retaining responsibility for education) 

In November 2006, Brash resigned as leader  English was considered as a potential replacement leader (running against John Key) or deputy leader (against incumbent Gerry Brownlee) in the ensuing leadership election  However, a contest was avoided when the MPs agreed a Key/English ticket would run unopposed in a display of party unity  English took over the deputy leadership and the finance portfolio in the Key shadow cabinet 

Fifth National Government (2008–2017)

Deputy Prime Minister and Minister of Finance (2008–2016)

At the 2008 election, English was re-elected by his electorate, winning by a margin of about 15,500 votes  He became Deputy Prime Minister of New Zealand and Minister of Finance in the fifth National Government, being sworn into office on 19 November 2008 and continued to serve in those roles until becoming Prime Minister on 12 December 2014  He was also made Minister of Infrastructure in National's first term of government and Minister responsible for Housing New Zealand Corporation and minister responsible for the New Zealand flag consideration process in its third 

He was comfortably re-elected in Clutha-Southland in the 2011 election but opted to run as a party-list candidate in 2014  

The pairing of John Key as leader of the National Party and English as his deputy has been compared to that of Bob Hawke and Paul Keating (in Australia) and Tony Blair and Gordon Brown (in the UK)



ground_truth: McPherson.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: 对于PD3.0协议，FS312BH支持的最高诱骗电压是多少？

Read the following text and answer briefly.

Context 3: '无锡速芯微电子有限公司是一家集芯片 研发，销售和服务于一体的国家高新技 术企业，为客户提供高性能，高集成 度，极致体验的全协议快充芯片。 无锡速芯微电子有限公司 FastSOC Microelectronics Co ,Ltd  销售联系方式： 联系人：顾先生 手机：1800 185 3071 邮箱：gpp@fastsoc com 网址：www fastsoc com 地址：无锡市新吴区菱湖大道200号中国物联网国际创新园E-503室 顾工微信号 速芯微公众号 免责声明：本文所述方法、方案均供客户参考，用于提示或者展示芯片应用的一种或者多种方式，不作为最终产品的实际方案。文中所描述的功能和性能指标在实 验室环境下测试得到，部分可以提供第三方测试报告，但是不保证客户产品上能获得相同的数据。本文信息只作为芯片使用的指导，不授权用户使用本公司或者其 他公司的知识产权。本文信息只作为芯片使用的指导，不承担因为客户自身应用不当而造成的任何损失。 **文中信息仅供参考，详情请联系我司获取最新资料” 无锡速芯微电子有限公司 FastSOC Microelectronics Co ,Ltd  产品手册 2023年 
新品快览 FS312A：PD3 0 诱骗- FS312A支持PD2 0/PD3 0最高诱骗电压：20V - FS312AE支持PD2 0/PD3 0 最高诱骗电压：20V支持Emarker模拟功能 - 封装：SOT23-5 VBUS CC1 CC2 DM DP 用电电路 4 7K 0 47uF R C C 1 V D D F U N C C C 2F S 3 1 2 B D M D P EP GND 应用图 FS8628：A+C快充协议CC2 CC1 VBUS CC2 CC1 FS312A FUNC GND VDD 4 7K GND R 用电电路 1uF GND 应用图 多口极简方案 FS8611SP*2+CCM-8611SP-A+7533B-T 双C智能降功率方案 FS8611S USB-C AC-DC 双变压器 7533B-T CCM-8611SP-A FS8611S USB-C 采用2颗FS8611SP搭配CCM-8611SP-A （MCU），7533B-T配合工作 - 支持多种协议 - 支持I2C控制 - 任意单 C 的为 35W - 双 插 降 功 率 ， 三 档 功 率 智 能 配 置：27 4W+7 4W；17 4W+17 4W； 27 4W - BOM极简，成本低 FS312B：PD3 1 诱骗FS8611K*2+CCM-8611K-A+7550B-T 双C方案 - FS312BL支持PD2 0/PD3 0/PD3 1/第三方协议最高诱骗电压：20V - FS312BLE支持PD2 0/PD3 0/PD3 1/第三方协议最高诱骗电压：20V支持Emarker模拟功能 - FS312BH支持PD2 0/PD3 0/PD3 1/第三方协议最高诱骗电压：48V - FS312BHE支持PD2 0/PD3 0/PD3 1/第三方协议最高诱骗电压：48V 支持Emarker模拟功能 - 封装：DFN2x2-6L - 兼容兼容BC1 2、Apple2 4A、 QC2 0 Class A、QC3 0 Class A/B、 FCP、SCP、AFC、低压直充等 - 兼容Type-C PD2 0、Type-C PD3 0、 Type-C PD3 0 PPS、QC4 0协议 - 支持两路DP/DM - 支持CV/CC（分段CC）功能 - 支持定制PDO - 支持A+C双口工作，电压自动回5V - 支持FB/OPTO反馈 - 封装：QFN3x3-20L VPWR FB PowerSystem 100K GND R1 GND 19 VIN 17 FB FUNC1 FUNC2 20 15 18 13 PLUGIND VFB FS8628 QFN3x3-20L AGATE 47K 7 5K 47K 7 5K 1 16 8 7 3 4 5 6 10 9 11 CGATE CVBUS CC2 CC1 CDP CDM AVBUS DM DP ISP ISN 12 应用图 2 V3P3 100Ω 1u EP GND GND CVBUS TYPE- C CC2 CC1 CDP CDM CGND TYPE-A AVBUS DM DP 10n 200 AGND 5mΩ GND FS8611K USB-C AC-DC DC-DC 7550B-T CCM-8611K-A FS8611K USB-C 采用2颗FS8611K搭配CCM-8611K-A （MCU）工作，7550B-T配合工作 - 支持PD2 0/PD3 0/QC2 0/AFC/FCP - 支持PDO定制 - 任意单 C 的为 35W(可定制) - 双插18W（可定制15W/20W） - BOM极简，成本低 FS212C+ACM-212C-A+7550B-T 双C方案 FS212C USB-C AC-DC DC-DC 7550B-T ACM-212C-A FS8623B-A+C方案 AC-DC DC-DC FS8623B USB-A USB-C USB-A 采 用 1 颗 F S 2 1 2 C 搭 配 ACM-212C-A 工 作，7550B-T配合工作 - 支持PD2 0/PD3 0 - 支持PDO定制 - 任意单 C 的为20W - 双插7 5W回5V - BOM极简，成本低 采用一颗FS8623B实现A+C方案 - 兼容兼容Apple2 4A/低压直充 QC2 0 Class A/QC3 0 Class A/B/ FCP/SCP等 - 兼 容Type -C PD2 0 / PD3 0 / PD3 0PPS/QC4 0协议 - 支持PDO定制 - 双插回5V 
多口方案选型 产品选型 受电端芯片选型 速芯微现有多种多口的方案选择：A+C，C+C，C+C+A，C+C+C，C+C+A+A等方案。对于 A+C的方案，可使用1颗芯片实现，也可用多颗芯片来实现。 速芯微现有多种受电端诱骗芯片，客户可根据应用需求进行选择。 受电端诱骗芯片应用领域 筋膜枪 无线充 线材 无人机 产品型号 PD2 0 PD3 0 PD3 1 第三方协议 诱骗电压(V) 控制方式 内置Emarker 定制 封装 FS312A √ √ 5/9/12/15/20 电阻阻值 可变电压策略 SOT23-5 FS312AE √ √ 5/9/12/15/20 电阻阻值 √ (公头专用) 可变电压策略 SOT23-5 FS312BL √ √ √ √ 5/9/12/15/20 电阻阻值 可变电压策略 DFN2x2-6 FS312BLE √ √ √ √ 5/9/12/15/20 电阻阻值 √ (公头专用) 可变电压策略 DFN2x2-6 FS312BH √ √ √ √ 5/20/28/36/48 电阻阻值 可变电压策略 DFN2x2-6 FS312BHE √ √ √ √ 5/20/28/36/48 电阻阻值 √ (公头专用) 可变电压策略 DFN2x2-6 FS312LC √ √ √ 5/9/12 电阻阻值 可变第三方 协议 SSOP10 FS312HC √ √ √ 5/9/12/15/20 电阻阻值 可变第三方 协议 SSOP10 FS2711Q √ √ √ 任意设置 I2C √ QFN3x3-16 FS2711P √ √ √ 任意设置 I2C √ QFN3x3-16 FS2711PA √ √ 全协议 任意设置 I2C √ SSOP10 FS2711SW √ √ 全协议 SSOP10 FS512 √ √ 全协议 任意设置 I2C √ SSOP10 方案 类型 产品型号 单C 单A 双插 A+C方案 FS8623 20W（PPS）（可定制） A口全协议18w 5V共享3A FS8623B 20W（PPS）(可定制) A口全协议18w 5V共享3A FS8628 20W（PPS）(可定制) A口全协议18w 5V共享3A FS8611RPC+FS116DB 65W（PPS）(可定制) A口全协议18w A口：5V/2 4A C口：45W FS8628RC+FS116DB 35W(可定制) A口全协议18w A口：5V（BC1 2，Apple 2 4） C口：20W 方案类型 产品型号 单C1 单C2 C1/C2 C+C方案 FS8611RPB*2 30W(可定制) 30W(可定制) C1/C2：5V/3A(或5V/2 4A) FS8611GH*2 35W（可定制） 35W（可定制） C1/C2:18W（可定制） FS8628P*2 35W（可定制） 35W（可定制） C1/C2：17 4W可定制） FS8611KL*2 20W（可定制） 20W（可定制） C1/C2：5V/1 5 A FS8611PC*2 35W 35W C1/C2：18W FS8611BH*2 65W（可定制） 65W（可定制） C1：45W（可定制）C2：20W（可定制） FS8628RPC+FS8611RB 45W（可定制）） 36W （可定制）） C1：30W（可定制）C2：5V/1 5A（可定制） 方案类型 产品型号 单C1 单C2 单A C1+C2 C1/C2+A C1+C2+A C+C+A FS8611S*2+FS116DB 65W（可定制） 65W（ 可定制）） A口全协议18w 智能分配功率 45W+18W C1/C2：智能分配功率 A：18W（或5V1 5A） FS8612C+FS8628P 100W（可定制） 35W （可定制）） 20W C1：65W C2：20W C1+A：65W+20W C2+A：7 5W+7 5W C1：65W C2：7 5W A：7 5W 其他 
Source-TYPE C协议芯片选型 Source-TYPE A协议芯片选型 速芯微现有多种TYPE-C的快充协议芯片，支持多种协议，支持客户定制，多样化，满 足客户对TYPE C的各种快充需求。 速芯微现有多种TYPE A快充协议芯片，支持全协议，支持定制，满足客户对A口协议的各种需 求。速芯微的TYPE-A快充协议芯片的协议丰富，FS112系列拥有多种的型号；FS116D 系列带插入指示，可搭配TYPE-C快充协议芯片，实现A+C，A+C+C，A+A+C+C等多口方 案，协议丰富，其中FS116A一般用于插入指示使用 Source-TYPE A协议芯片引脚封装图 D+ VSS FB 1 2 3 FS112 6 5 4 D- VDD FUNC GATE VIN FUNC FB LED/PLUG_IN 1 2 3 4 5 FS116D 10 DM 9 8 7 6 DP CSP CSN VSS速芯微的各TYPE-C快充协议芯片之间可搭配使用，实现多口方案，更多详情请咨 询我司工作人员。 多口降功率专用快充协议芯片：FS8611RB，FS8611RC，FS8611RPB，FS8611RPC， FS8612CP。 带I2C快充协议芯片：FS8611S，FS8611SP 产品型号 BC1 2 Apple 2 4 QC2 0 QC3 0 AFC FCP SCP HISCP 大电流直充 封装 FS112 √ √ √ √ √ √ √ SOT23-6 FS112H √ √ √ √ √ √ √ √ √ SOT23-6 FS113 √ v √ √ √ √ √ √ √ SOT23-6 FS116DP √ √ √ √ √ √ √ √ SSOP10 FS116DB √ √ √ √ √ √ √ √ SSOP10 FS116E √ √ √ √ √ √ √ √ √ SSOP10 FS116A √ √ SSOP10 其他 可定制 PD2 0 PD3 0 PD3 0 PPS 第三方协议 反馈方式 MOS CV/CC 定制 封装 FS212C √ √ FB √ SOT23-6 FS212CM √ √ FB PMOS(可省) √ SOT23-6 FS212D √ √ √ FB √ SOT23-6 FS212DH √ √ √ FB √ SOT23-6 FS212DP √ √ √ FB PMOS √ SOT23-6 FS212DG √ √ √ FB PMOS √ SOT23-6 FS8611G √ √ FB PMOS(可省) √ SOP-8 FS8611K √ √ QC2 0/AFC/FCP FB PMOS(可省) √ SOP8 FS8611J √ √ √ 全协议 FB PMOS(可省) √ SOP8 FS8611B √ √ √ 全协议 FB PMOS(可省) √ SSOP10 FS8611RB √ √ 全协议 FB PMOS √ SSOP10 FS8611RC √ √ 全协议 FB PMOS √ SSOP10 FS8611S √ √ √ 全协议 FB PMOS √ SSOP10 FS8611PP √ √ √ 全协议 FB PMOS √ SSOP10 FS8611BP √ √ √ 全协议 FB PMOS(可省) √ SSOP10 FS8611RPB √ √ √ 全协议 FB PMOS √ SSOP10 FS8611RPC √ √ √ 全协议 FB PMOS √ SSOP10 FS8611SP √ √ √ 全协议 FB PMOS(可省) SSOP10 FS8612 √ √ √ 全协议 OPTO PMOS √ √ SSOP16 FS8612B √ √ √ 全协议 FB PMOS √ √ SSOP16 FS8612BP √ √ √ 全协议 FB PMOS √ √ SSOP16 FS8612C √ √ √ 全协议 FB/OPTO PMOS √ √ QFN4x4-16 FS8612CP √ √ √ 全协议 FB/OPTO PMOS √ √ QFN4x4-16 
'



ground_truth: 48V.