A quick introduction

The intention of Textplumber is to make it easy to extract features from text data as part of a Sci-kit learn pipeline. It allows you to extract different kinds of features extracted from text, which you can combine as needed. This example demonstrates some basic functionality. If you are accessing this example from the documentation site, you can download the notebook from Github.

You can install Textplumber using pip …

pip install textplumber

# Note: the directive above is used to prevent the code being executed during release.
# If you have downloaded the notebook for your own use, you can remove the directive,
# but this is not necessary (it is just a comment).
import os
from datasets import load_dataset
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.model_selection import train_test_split
from sklearn.feature_selection import SelectKBest, mutual_info_classif, chi2
from sklearn.pipeline import FeatureUnion
from sklearn.datasets import fetch_20newsgroups

from textplumber.core import *
from textplumber.clean import *
from textplumber.preprocess import *
from textplumber.tokens import *
from textplumber.pos import *
from textplumber.embeddings import *
from textplumber.report import *
from textplumber.store import *
from textplumber.lexicons import *
from textplumber.textstats import *

1. Load example data

First, let’s load some data from the AuthorMix dataset. This dataset includes samples of fiction (‘author’), speeches (‘speech’) and blog posts (‘blog’).

X_train, y_train, X_test, y_test, target_classes, target_names = get_example_data(target_labels = ['blog', 'speech'])

2. Setup feature store and classification pipeline

Next, define an SQLite database to store features so we can avoid recomputing them every time we run the pipeline via TextFeatureStore. A feature store is required for most of the components in this package.

feature_store = TextFeatureStore('basic-introduction-store.sqlite')

In the example pipeline, the texts are cleaned with TextCleaner, then preprocessed using SpacyPreprocessor, and then features based on token counts are extracted using TokensVectorizer.

Below we train a model and evaluate it on the test set. For a nicely labelled confusion matrix use plot_confusion_matrix.

pipeline = Pipeline([
    ('cleaner', TextCleaner(strip_whitespace=True)),
    ('preprocessor', SpacyPreprocessor(feature_store=feature_store, pos_tagset = 'detailed')),
    ('tokens', TokensVectorizer(feature_store=feature_store, max_features=200)),
    ('classifier', LogisticRegression(max_iter=5000, random_state=4)),
], verbose=True)

display(pipeline)

Pipeline(steps=[('cleaner', TextCleaner(strip_whitespace=True)),
                ('preprocessor',
                 SpacyPreprocessor(feature_store=<textplumber.store.TextFeatureStore object at 0x7f24cb16cad0>,
                                   pos_tagset='detailed')),
                ('tokens',
                 TokensVectorizer(feature_store=<textplumber.store.TextFeatureStore object at 0x7f24cb16cad0>,
                                  max_features=200)),
                ('classifier',
                 LogisticRegression(max_iter=5000, random_state=4))],
         verbose=True)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

3. Train and evaluate a model

pipeline.fit(X_train, y_train)
y_pred = pipeline.predict(X_test)

[Pipeline] ........... (step 1 of 4) Processing cleaner, total=   0.0s
[Pipeline] ...... (step 2 of 4) Processing preprocessor, total=  35.9s
[Pipeline] ............ (step 3 of 4) Processing tokens, total=   1.5s
[Pipeline] ........ (step 4 of 4) Processing classifier, total=   0.5s

print(classification_report(y_test, y_pred, labels = target_classes, target_names = target_names, digits=3))
plot_confusion_matrix(y_test, y_pred, target_classes, target_names)

              precision    recall  f1-score   support

        blog      0.931     0.935     0.933       981
      speech      0.913     0.908     0.911       740

    accuracy                          0.923      1721
   macro avg      0.922     0.921     0.922      1721
weighted avg      0.923     0.923     0.923      1721

4. A more complex pipeline

Let’s create a more complex pipeline with different kinds of features.

This pipeline combines tf-idf weighted token features, features based on bigrams of part-of-speech tags, and embeddings. The pipeline applies feature selection using standard Scikit-learn components.

pipeline = Pipeline([
    ('cleaner', TextCleaner(strip_whitespace=True)),
    ('preprocessor', SpacyPreprocessor(feature_store=feature_store, pos_tagset = 'detailed')),
    ('features', FeatureUnion([

        ('tokens', Pipeline([
            ('token_vectors',  TokensVectorizer(feature_store=feature_store, vectorizer_type='tfidf')),
            ('selector', SelectKBest(score_func=chi2, k=200)),            
            ], verbose=True)
        ),

        ('pos', Pipeline([
            ('pos_vectors',  POSVectorizer(feature_store=feature_store, ngram_range = (2, 2))),
            ('selector', SelectKBest(score_func=mutual_info_classif, k=120)),
            ], verbose=True)
        ),

        ('textstats', 
            Pipeline([
                ('textstats_vectors', TextstatsTransformer(feature_store=feature_store)), 
                ('selector', SelectKBest(score_func=mutual_info_classif, k=6)),
                ], verbose = True)),

        ('embeddings', Model2VecEmbedder(feature_store=feature_store)),
    ], verbose=True)),
    ('scaler', StandardScaler(with_mean=False)),
    ('classifier', LogisticRegression(max_iter=5000, random_state=4)),
], verbose=True)

display(pipeline)

pipeline.fit(X_train, y_train)
y_pred = pipeline.predict(X_test)

[Pipeline] ........... (step 1 of 5) Processing cleaner, total=   0.1s
[Pipeline] ...... (step 2 of 5) Processing preprocessor, total=   0.5s
[Pipeline] ..... (step 1 of 2) Processing token_vectors, total=   1.4s
[Pipeline] .......... (step 2 of 2) Processing selector, total=   0.0s
[FeatureUnion] ........ (step 1 of 4) Processing tokens, total=   1.4s
[Pipeline] ....... (step 1 of 2) Processing pos_vectors, total=   1.3s
[Pipeline] .......... (step 2 of 2) Processing selector, total=   3.1s
[FeatureUnion] ........... (step 2 of 4) Processing pos, total=   4.4s
[Pipeline] . (step 1 of 2) Processing textstats_vectors, total=   0.5s
[Pipeline] .......... (step 2 of 2) Processing selector, total=   0.3s
[FeatureUnion] ..... (step 3 of 4) Processing textstats, total=   0.8s
[FeatureUnion] .... (step 4 of 4) Processing embeddings, total=   1.3s
[Pipeline] .......... (step 3 of 5) Processing features, total=   7.9s
[Pipeline] ............ (step 4 of 5) Processing scaler, total=   0.0s
[Pipeline] ........ (step 5 of 5) Processing classifier, total=   1.0s

print(classification_report(y_test, y_pred, labels = target_classes, target_names = target_names, digits=3))
plot_confusion_matrix(y_test, y_pred, target_classes, target_names)

              precision    recall  f1-score   support

        blog      0.966     0.973     0.970       981
      speech      0.964     0.954     0.959       740

    accuracy                          0.965      1721
   macro avg      0.965     0.964     0.964      1721
weighted avg      0.965     0.965     0.965      1721

Notice that the preprocessor step is significantly faster, as we are getting the features from the feature store.

5. Inspect features

For a logistic regression classifier you can inspect discriminating features with plot_logistic_regression_features_from_pipeline.

plot_logistic_regression_features_from_pipeline(pipeline, target_classes, target_names, top_n=20, classifier_step_name = 'classifier', features_step_name = 'features')

	Feature	Log Odds (Logit)	Odds Ratio
8	tokens__--	-1.643806	0.193243
1	tokens__"	1.477502	4.381987
478	embeddings__emb_152	1.447648	4.253098
327	embeddings__emb_1	-1.415513	0.242801
466	embeddings__emb_140	-1.315602	0.268313
336	embeddings__emb_10	1.255531	3.509701
325	textstats__hapax_legomena_to_unique	1.198631	3.315575
11	tokens__....	1.171189	3.225826
333	embeddings__emb_7	-1.120871	0.325996
145	tokens__our	-1.092892	0.335245
356	embeddings__emb_30	-0.932755	0.393468
323	textstats__average_tokens_per_sentence	0.930154	2.534900
332	embeddings__emb_6	0.927065	2.527082
457	embeddings__emb_131	-0.917831	0.399384
21	tokens__And	-0.904820	0.404615
320	textstats__characters_count	0.880371	2.411793
547	embeddings__emb_221	-0.863576	0.421651
174	tokens__though	0.863480	2.371399
231	pos__CC VBZ	0.847181	2.333061
387	embeddings__emb_61	-0.822505	0.439330

To understand the process of feature selection and extraction through a pipeline, use preview_pipeline_features …

preview_pipeline_features(pipeline)

cleaner TextCleaner

This step receives and returns text.

preprocessor SpacyPreprocessor

This step receives and returns text.

features FeatureUnion

token_vectors TokensVectorizer

Features Out (5000)

!, ", #, $, %, &, ', '', 'cause, 'd, 'em, 'll, 'm, 're, 's, 've, (, ), *, +, ,, -, --, ., .., ..., ...., ....., ......, ......., ........, /, //.etro, 0, 1, 1,000, 10, 10,000, 100, 100,000, 11, 11th, 12, 13, 14, 15, 16, 17, 18, 19, 1980, 1990s, 1991, 2, 20, 200, 2000, 2001, 2002, 2003, 2004, 2011, 2016, 20th, 21st, 22, 23, 24, 25, 250, 250,000, 26, 27, 3, 3,000, 30, 300, 35, 4, 4,000, 40, 400, 45, 5, 5'9, 5,000, 50, 50,000, 500, 55, 6, 60, 600, 65, 7, 7,000, 70, 700, 716, 8, 80, 85, 9, 9/11, 90, 95, :, ;, =, >, ?, @, A, A., AIDS, AND, Aaron, Abel, About, Above, Academy, According, Act, Actually, Adam, Afghan, Afghanistan, Africa, African, After, Again, Agency, Ah, Al, Alabama, Alex, Alexander, Alexandre, All, Allen, Almost, Along, Already, Also, Although, Alyx, America, American, Americans, Americas, Among, Amy, An, And, Angeles, Another, Anthony, Anupra, Any, Anyone, Anyway, Anyways, Apple, April, Arab, Arabia, Are, Arizona, Armed, Army, Around, As, Asia, Asian, Ask, Assad, Assembly, At, August, Aura, Avatar, B., Back, Baghdad, Baghdadi, Bank, Barrett, Barry, Bay, Because, Before, Behind, Being, Ben, Berlin, Best, Better, Bible, Biden, Bill, Birx, Black, Bless, Bob, Boehner, Book, Books, Border, Boston, Both, Boy, Brazil, Brian, British, Brown, Bucko, Building, Burma, Bush, But, By, C., CDC, CEO, CEOs, COVID-19, Ca, Cabal, Cadence, Cairo, California, Cally, Can, Canada, Canadian, Capitol, Carol, Carolina, Cash, Cassius, Catherine, Center, Central, Chairman, Chambers, Chandler, Chantry, Charice, Chase, Chicago, Chief, China, Chinese, Christ, Christian, Christians, Christmas, Chuck, Church, City, Civil, Clark, Class, Clinton, Club, Coast, Cold, College, Come, Commander, Compton, Congress, Connecticut, Connelly, Connie, Constitution, Corps, Could, Council, County, Court, Cross, Cuba, Cuban, Cubans, D., DACA, Daisy, Damn, Dave, David, Day, Deborah, December, Defense, Democrat, Democratic, Democrats, Department, Depression, Derelict, Despite, Detroit, Devros, Diaries, Did, Director, Do, Doctor, Does, Don, Donald, Downtown, Dr, Dr., Dream, During, E., Each, Earth, East, Eastern, Eddie, Edinburgh, Egypt, Either, El, Election, Elizabeth, Emmerich, Energy, England, English, Enochian, Escalus, Europe, European, Even, Eventually, Every, Everyone, Everything, Excuse, Eyes, FBI, FDA, February, Federal, Feeling, Fforde, Finally, First, Five, Florida, For, Force, Forces, Four, Fourth, France, Freedom, French, Friday, Friedrich, From, Fuck, G., Gabby, Gambit, Gavin, General, George, Georgia, German, Germany, Get, Go, God, Goddess, Good, Google, Got, Government, Governor, Governors, Grandmother, Great, Griswold, Group, Guard, Gulf, H., HIV, HYPE, Had, Hall, Hamas, Hana, Happy, Harrison, Harry, Haslett, Have, Having, He, Head, Health, Heaven, Hell, Hello, Hepzibah, Her, Here, Hey, Hi, High, Hiro, His, Hispanic, History, Hitler, Hmm, Holiday, Holy, Home, Homeland, Hope, Hopefully, Hoping, Hours, House, Houston, How, However, Huh, Human, Hussein, I, I'm, I., ICE, ID, II, IS, ISIS, Iain, Ian, If, Illinois, Imagine, Imogen, In, Inc., Indeed, Independent, India, Indian, Indonesia, Information, Initiative, Inn, Instead, International, Internet, Invisible, Iowa, Iran, Iranian, Iraq, Iraqi, Iraqis, Is, Isaac, Islam, Islamic, Island, Isobel, Israel, Israeli, Israelis, It, Italy, Its, J., Jack, Jacob, Jake, Jamaia, James, Jane, January, Japan, Japanese, Jarrod, Jarvis, Jason, Jenkins, Jennifer, Jerusalem, Jess, Jesse, Jesus, Jesyca, Jet, Jewish, Jews, Jim, Job, Jobs, Joe, John, Johnson, Jon, Jonah, Jonathan, Jones, Jordan, Joseph, Josh, Jr., Juclecia, Judge, July, June, Juska, Just, Justice, Kasui, Kaze, Keene, Kennedy, Kevin, Kim, King, Kingdom, Korea, Korean, Kraus, Kuwait, Laden, Lady, Langer, Last, Later, Law, Leader, Lee, Lehane, Let, Liberty, Library, Light, Like, Likewise, Lilly, Little, London, Long, Look, Looking, Lord, Lori, Los, Lost, Louisiana, Luther, MSN, Main, Majority, Make, Manhattan, Many, Marcus, Mark, Marshall, Martin, Mary, Maryland, Matt, Matthew, May, Maybe, Mayor, McCain, McLaughlin, Me, Meanwhile, Measurement, Medicaid, Medicare, Members, Men, Mercutio, Mexico, Michael, Michelle, Michigan, Middle, Mike, Millia, Millions, Mind, Minister, Miss, Mississippi, Mitch, Monday, Monkey, Moon, More, Moskowitz, Most, Mother, Mr., Mrs., Ms., Music, Muslim, Muslims, My, Mystic, N., NASA, NATO, Nancy, Nanny, Nation, National, Nations, Natoma, Navy, Nazi, Nevada, Never, New, Newtown, Next, Nick, Night, Niko, No, Nobody, Nora, North, Not, Nothing, November, Novus, Now, OF, OK, OOC, Obama, Obamacare, Obviously, October, Of, Office, Officer, Oh, Ohio, Oi, Ok, Okay, On, Once, One, Only, Operation, Or, Order, Organization, Orleans, Osama, Other, Others, Otto, Our, Over, P., Pacific, Pakistan, Pakistani, Palestinian, Palestinians, Pants, Paris, Party, Pass, Patrol, Paul, Peace, Pelecanos, Pelosi, Pence, Peninsula, Pennsylvania, Penny, Pentagon, Penthesilia, People, Perhaps, Peter, Philadelphia, Plan, Please, Pobble, Pobs, Poe, Poetry, Point, Ponderland, Post, Powell, President, Presidents, Press, Prime, Prize, Probably, Public, Put, Qaeda, Qaida, REALLY, Random, Rankin, Rather, Reaching, Reading, Reagan, Really, Realm, Rebus, Red, Reich, Remember, Representatives, Republic, Republican, Republicans, Rhiamon, Rice, Ridge, Right, Rights, River, Robert, Roche, Ronald, Room, Roosevelt, Rose, Russia, Ryan, S., SC, Saddam, San, Saturday, Saudi, Scalia, School, Science, Scott, Scout, Scouts, Scripture, Seat, Second, Secretary, Security, See, Selma, Senate, Senator, Sept., September, Sergeant, Service, Services, Seven, Several, She, Sheldon, Should, Side, Simon, Since, Siomen, Six, Small, Smith, So, Social, Soleimani, Some, Someone, Something, Sometimes, Soon, Sorry, Sounds, South, Southern, Soviet, Space, Speaker, Staff, Standingwater, Star, Starbucks, Starting, State, States, Steve, Still, Street, Such, Sunday, Supreme, Sure, Susan, Syria, Syrian, T., TC, THAT, THE, TV, Take, Tal, Taliban, Taliesin, Ted, Teddy, Tell, Texas, Thank, Thanks, That, The, Their, Then, There, These, They, Think, Third, This, Thomas, Thompson, Those, Though, Three, Through, Throughout, Thursday, Thus, Time, Times, Tiziano, To, Today, Together, Tom, Tomorrow, Tonight, Too, Toronto, Trade, Treasury, True, Truman, Trump, Tunisia, Two, U., USA, Ugh, Uh, Under, Unfortunately, Union, United, University, VA, VERY, Valnastium, Venezuela, Very, Vice, Vietnam, Viktor, Virginia, Vulture, W., Wait, Wall, Walter, Want, War, Wars, Was, Washington, We, Web, Wednesday, Week, Welcome, Well, Wendy, West, What, Whatever, When, Where, Whether, Which, While, White, Who, Why, Will, William, Williams, With, Without, Work, World, Would, Wow, X, Xi, Yale, Yeah, Yemen, Yes, Yesterday, Yet, York, You, Your, [, ], ^_^., _, a, a.m., abandon, abandoned, abiding, ability, able, about, above, abroad, absence, absolutely, abuse, academic, accelerate, accent, accept, accepted, accepting, accepts, access, accident, accompanied, accompany, accomplish, accomplished, according, account, accountability, accountable, accounts, accurate, achieve, achieved, achievement, achievements, achieving, acid, acknowledge, across, act, acted, acting, action, actions, active, actively, activities, activity, acts, actual, actually, adapt, add, added, addiction, adding, addition, additional, address, addressing, adds, administration, administrations, admit, admitted, adopted, adult, adults, advance, advanced, advances, advancing, advantage, advice, afar, affairs, affect, affected, affection, affiliates, afford, affordable, afraid, after, afternoon, afterwards, again, against, age, aged, agencies, agency, agenda, agent, agents, ages, aggression, aggressive, aging, ago, agree, agreed, agreement, agreements, agrees, ahead, ai, aid, aimed, air, airports, aisle, al, alcohol, aliens, align, alike, alive, all, alliances, allies, allow, allowed, allowing, allows, ally, almost, alone, along, alongside, already, alright, also, alternative, although, altogether, always, am, amazing, ambitions, ambitious, amidst, among, amount, amounts, amusing, an, analysis, ancient, and, anger, angry, announce, announced, announcing, annual, another, answer, answered, answers, anti, any, anybody, anymore, anyone, anything, anyway, anyways, anywhere, apart, apartment, apparent, apparently, appeal, appear, appears, apply, appointed, appreciate, appreciation, approach, approaches, approaching, appropriate, approve, approved, approximately, arbitrary, are, area, areas, argue, argued, arguments, arm, armed, armies, arms, army, aroma, around, arrested, arrival, arrive, arrived, arrives, art, artist, arts, as, ash, ashes, aside, ask, asked, asking, asks, asleep, aspects, aspirations, ass, assembly, assistance, assume, assuming, assure, assured, astronauts, at, ate, atmosphere, attack, attacked, attacks, attempt, attempted, attempts, attend, attention, attitude, audience, author, authorities, authority, authors, auto, automakers, available, average, avoid, aware, away, awful, baby, back, background, backgrounds, backpack, backs, backward, backwards, bad, badly, bag, bags, balance, balanced, ball, ballot, ballots, ban, band, bank, banks, banning, bar, barely, bargain, barrier, barriers, base, based, basement, bases, basic, basically, basis, bathroom, battle, battles, bay, be, bear, bears, beat, beating, beats, beautiful, beauty, became, because, become, becomes, becoming, bed, beds, been, beer, beers, before, began, begin, beginning, beginnings, begins, begun, behalf, behavior, behind, being, beings, belief, beliefs, believe, believed, believes, belong, belongs, beloved, below, belt, bench, bending, bends, beneath, benefit, benefits, beside, best, bet, better, between, beyond, big, bigger, biggest, bill, billion, billions, bills, bin, biological, bipartisan, birth, birthday, bit, bitch, bite, bitter, black, blame, blank, bless, blessed, blind, blinking, blinks, block, blocking, blog, blogging, blood, bloodied, bloody, blow, blows, blue, board, bodies, body, bold, bomb, bonds, bones, book, books, booksellers, bookstore, boom, booming, boost, boot, boots, border, borders, bored, boring, born, borne, both, bother, bottle, bottom, bought, bounce, bouncing, bound, bowl, box, boxes, boy, boyfriend, boys, brain, brand, brave, bread, break, breakfast, breaking, breaks, breakthroughs, breast, breath, bridge, bridges, brief, briefly, bright, brighter, brightest, brightly, brilliant, bring, bringing, brings, broad, broader, broke, broken, brother, brothers, brought, brow, brown, brutal, bucks, budget, budgets, build, building, buildings, built, bullet, bunch, burden, burdens, bureaucracy, bureaucrats, buried, burn, burned, burning, bus, business, businesses, busy, but, butt, butter, buy, buying, by, ca, cab, cabal, caliphate, call, called, calling, calls, calm, calories, came, camera, campaign, camps, can, cancer, candidate, cap, capabilities, capacity, capital, captured, car, card, cardboard, cards, care, career, careers, careful, carefully, cares, caring, carried, carry, carrying, cars, case, cases, cash, cast, casual, casually, cat, catch, catches, caught, cause, caused, causes, causing, cave, cavern, cease, ceiling, celebrate, celebrities, celebrity, cell, cellphone, cells, center, central, centuries, century, ceremony, certain, certainly, certainty, chain, chains, chair, chairs, challenge, challenged, challenges, challenging, chamber, chambers, chance, chances, change, changed, changes, changing, chaos, character, characters, charge, charged, charities, charity, chart, charts, chatted, cheap, check, checks, cheeks, cheese, chemical, cherish, cherished, cherry, chest, chicken, chief, child, childhood, children, chill, chin, chips, choice, choices, choose, chorus, chose, chosen, chuckles, church, cigar, cigarette, cigarettes, circle, circles, circumstances, cities, citizen, citizens, citizenship, city, civil, civilians, civilization, civilized, claim, claims, clarify, clarity, class, classes, classic, clean, cleaner, cleaning, clear, clearly, climate, climb, clock, close, closed, closely, closer, closes, closest, closet, closing, cloud, club, clubs, co, coal, coalition, coat, cocaine, code, coffee, cold, collapse, collar, colleagues, collection, collective, college, colleges, color, combat, combination, combined, come, comes, comfort, comfortable, coming, comment, comments, commerce, commercial, commission, commit, commitment, commitments, committed, committee, common, communism, communities, community, companies, company, compared, compassion, compassionate, compel, compete, competition, competitive, complete, completed, completely, complex, complicated, comprehensive, compromise, computer, concept, concern, concerned, concerns, conclusion, concrete, condition, conditions, conduct, conference, confidence, confident, confirmation, confirmed, conflict, conflicts, confront, confronted, confronting, confused, congressional, congressmen, connect, connected, connecting, connection, conquer, conscience, consequence, consequences, conservative, consider, considerable, considerably, considered, considering, consistent, constant, constantly, constitutional, construction, consumers, contact, contained, containing, contains, contemporary, contents, context, continent, continue, continued, continues, continuing, continuous, contrary, contrast, contribute, contributing, contributions, control, controlled, controversy, conversation, conversations, conviction, convinced, coo, cook, cool, cooperate, cooperation, cop, copies, cops, copy, cord, core, corner, corners, coronavirus, corporate, corporations, corpse, correct, corridor, corrupt, corruption, cost, costly, costs, coughs, could, count, counter, counting, countless, countries, country, counts, county, couple, courage, course, court, courts, cover, coverage, covered, covering, covers, crack, crap, crazy, cream, create, created, creates, creating, creation, creative, creativity, credit, credits, creed, cried, crime, crimes, criminal, criminals, crisis, critical, cross, crossing, crowd, crucial, cry, crying, cue, cultural, culture, cultures, cup, cures, curious, curiously, current, currently, curve, customers, cut, cuts, cutting, cynical, cynicism, dad, daily, damage, damn, damned, dance, danger, dangerous, dangers, daring, dark, darkness, dash, data, date, daughter, daughters, day, days, de, dead, deadly, deal, dealers, dealing, deals, death, deaths, debate, debates, debt, debut, decade, decades, decent, decide, decided, decides, decision, decisions, decisive, declared, decline, declining, dedicated, dedication, deeds, deep, deeper, deepest, deeply, defeat, defeated, defeating, defend, defense, deficit, deficits, define, defined, defining, definitely, degree, degrees, deliberate, delicious, deliver, delivered, delivery, demand, demanding, demands, democracies, democracy, democratic, denied, deny, department, depend, depending, depends, deploy, deployed, depth, depths, describe, described, describes, description, deserve, deserves, design, designed, desire, desk, despair, desperately, despite, destiny, destroy, destroyed, destroying, destroys, destruction, detail, detailed, details, detective, determination, determine, determined, develop, developed, developing, development, devoted, devotion, diabetes, diagnosed, dialogue, dictator, dictators, dictatorship, did, die, died, diet, dieting, diff, difference, differences, different, differently, difficult, dig, dignity, digs, dime, dinner, diploma, diplomacy, direct, directed, direction, directly, director, directs, dirty, disagree, disappeared, disappearing, disarm, disaster, discipline, discover, discovered, discoveries, discovery, discretionary, discuss, discussed, discussing, discussion, discussions, disease, diseases, disrupt, distance, distant, distinguished, districts, disturbing, diverse, diversity, divide, divided, divine, division, do, doctor, doctors, documents, does, dog, doing, dollar, dollars, domestic, dominate, done, door, doors, doorway, dose, dot, double, doubled, doubling, doubt, down, dozen, draft, drag, dragged, drama, dramatic, dramatically, draw, drawing, drawn, draws, dream, dreams, dress, dressed, drift, drink, drinking, drinks, drive, driven, driver, drives, driving, drop, dropped, dropping, drops, drug, drugs, drunk, dry, duct, due, dull, dumb, dun, dunno, during, dust, duties, duty, dying, dynamic, e, each, ear, earlier, early, earn, earned, ears, earth, ease, easier, easily, easy, eat, eaten, eating, economic, economies, economy, edge, edges, editor, editors, educate, educated, education, effect, effective, effects, efficiency, efficient, effort, efforts, eh, eight, either, elbows, elected, election, elections, electric, element, elements, eliminate, eliminated, eliminating, else, elsewhere, email, embassy, embrace, embraced, embryonic, embryos, emerge, emergency, emotional, emotions, employed, employees, employer, employers, employment, empower, empty, enable, encourage, encouraged, encouraging, end, ended, ending, endless, ends, endure, enduring, enemies, enemy, energy, enforce, enforcement, engaged, engagement, engine, engineering, enjoy, enjoyed, enormous, enough, ensure, ensuring, enter, entered, entering, enterprise, enters, enthusiastic, entire, entirely, entrance, entrepreneurs, entries, entry, envelope, environment, epidemic, equal, equality, equally, equipment, era, err, escape, escaped, especially, essence, essential, essentially, establish, established, establishment, estate, etc, eternal, ethical, ethnic, evacuees, even, evening, event, events, eventually, ever, every, everybody, everyone, everything, everywhere, evidence, evident, evil, evolution, exact, exactly, exam, examine, example, exams, except, exchange, excited, exciting, excuse, excuses, executive, executives, exercise, exhales, exhausted, exist, existing, exists, exit, exits, expand, expanded, expanding, expanse, expansion, expect, expected, expecting, expense, 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VBD, IN VBG, IN VBN, IN VBP, IN VBZ, IN WDT, IN WP, IN WRB, IN XX, IN ``, JJ $, JJ '', JJ ,, JJ -LRB-, JJ -RRB-, JJ ., JJ :, JJ CC, JJ CD, JJ DT, JJ EX, JJ HYPH, JJ IN, JJ JJ, JJ JJR, JJ JJS, JJ MD, JJ NFP, JJ NN, JJ NNP, JJ NNPS, JJ NNS, JJ PDT, JJ POS, JJ PRP, JJ PRP$, JJ RB, JJ RBR, JJ RBS, JJ RP, JJ SYM, JJ TO, JJ UH, JJ VB, JJ VBD, JJ VBG, JJ VBN, JJ VBP, JJ VBZ, JJ WDT, JJ WP, JJ WRB, JJ ``, JJR '', JJR ,, JJR -RRB-, JJR ., JJR :, JJR CC, JJR CD, JJR DT, JJR HYPH, JJR IN, JJR JJ, JJR MD, JJR NN, JJR NNP, JJR NNPS, JJR NNS, JJR PRP, JJR RB, JJR RP, JJR TO, JJR VB, JJR VBD, JJR VBG, JJR VBP, JJR VBZ, JJR WRB, JJS '', JJS ,, JJS -RRB-, JJS ., JJS :, JJS CC, JJS CD, JJS DT, JJS EX, JJS HYPH, JJS IN, JJS JJ, JJS MD, JJS NN, JJS NNP, JJS NNPS, JJS NNS, JJS PDT, JJS PRP, JJS PRP$, JJS RB, JJS TO, JJS VBD, JJS VBG, JJS VBN, JJS VBP, JJS VBZ, JJS WDT, LS ,, LS -RRB-, LS ., LS :, LS DT, LS LS, LS NNP, LS PRP, MD '', MD ,, MD -LRB-, MD -RRB-, MD ., MD :, MD ADD, MD CC, MD CD, MD DT, MD EX, MD HYPH, MD IN, MD JJ, MD MD, MD NFP, MD NN, MD NNP, MD NNS, MD PRP, MD RB, MD RBR, MD RBS, MD TO, MD VB, MD VBD, MD VBG, MD VBN, MD ``, NFP '', NFP -RRB-, NFP ., NFP :, NFP ADD, NFP CC, NFP CD, NFP DT, NFP EX, NFP IN, NFP JJ, NFP MD, NFP NFP, NFP NN, NFP NNP, NFP NNS, NFP PDT, NFP PRP, NFP PRP$, NFP RB, NFP TO, NFP UH, NFP VB, NFP VBD, NFP VBG, NFP VBN, NFP VBP, NFP VBZ, NFP WDT, NFP WP, NFP XX, NFP ``, NN $, NN '', NN ,, NN -LRB-, NN -RRB-, NN ., NN :, NN CC, NN CD, NN DT, NN EX, NN HYPH, NN IN, NN JJ, NN JJR, NN JJS, NN MD, NN NFP, NN NN, NN NNP, NN NNPS, NN NNS, NN PDT, NN POS, NN PRP, NN PRP$, NN RB, NN RBR, NN RP, NN SYM, NN TO, NN UH, NN VB, NN VBD, NN VBG, NN VBN, NN VBP, NN VBZ, NN WDT, NN WP, NN WP$, NN WRB, NN XX, NN ``, NNP $, NNP '', NNP ,, NNP -LRB-, NNP -RRB-, NNP ., NNP :, NNP ADD, NNP CC, NNP CD, NNP DT, NNP EX, NNP FW, NNP HYPH, NNP IN, NNP JJ, NNP JJR, NNP JJS, NNP MD, NNP NFP, NNP NN, NNP NNP, NNP NNPS, NNP NNS, NNP PDT, NNP POS, NNP PRP, NNP PRP$, NNP RB, NNP RBR, NNP RBS, NNP RP, NNP SYM, NNP TO, NNP UH, NNP VB, NNP VBD, NNP VBG, NNP VBN, NNP VBP, NNP VBZ, NNP WDT, NNP WP, NNP WP$, NNP WRB, NNP XX, NNP ``, NNPS '', NNPS ,, NNPS -LRB-, NNPS -RRB-, NNPS ., NNPS :, NNPS CC, NNPS CD, NNPS DT, NNPS HYPH, NNPS IN, NNPS JJ, NNPS JJR, NNPS MD, NNPS NFP, NNPS NN, NNPS NNP, NNPS NNPS, NNPS NNS, NNPS PDT, NNPS POS, NNPS PRP, NNPS PRP$, NNPS RB, NNPS RP, NNPS TO, NNPS VB, NNPS VBD, NNPS VBG, NNPS VBN, NNPS VBP, NNPS VBZ, NNPS WDT, NNPS WP, NNPS WP$, NNS $, NNS '', NNS ,, NNS -LRB-, NNS -RRB-, NNS ., NNS :, NNS CC, NNS CD, NNS DT, NNS EX, NNS HYPH, NNS IN, NNS JJ, NNS JJR, NNS JJS, NNS MD, NNS NFP, NNS NN, NNS NNP, NNS NNPS, NNS NNS, NNS PDT, NNS POS, NNS PRP, NNS PRP$, NNS RB, NNS RBR, NNS RBS, NNS RP, NNS SYM, NNS TO, NNS VB, NNS VBD, NNS VBG, NNS VBN, NNS VBP, NNS VBZ, NNS WDT, NNS WP, NNS WP$, NNS WRB, NNS XX, NNS ``, PDT DT, PDT PRP$, PDT RB, PDT WDT, POS ,, POS -LRB-, POS ., POS :, POS CC, POS CD, POS DT, POS IN, POS JJ, POS JJR, POS JJS, POS MD, POS NN, POS NNP, POS NNPS, POS NNS, POS PRP, POS RB, POS RBR, POS RBS, POS UH, POS VB, POS VBD, POS VBG, POS VBN, POS VBP, POS VBZ, POS WDT, POS ``, PRP '', PRP ,, PRP -LRB-, PRP -RRB-, PRP ., PRP :, PRP CC, PRP CD, PRP DT, PRP EX, PRP FW, PRP HYPH, PRP IN, PRP JJ, PRP JJR, PRP JJS, PRP MD, PRP NFP, PRP NN, PRP NNP, PRP NNPS, PRP NNS, PRP PDT, PRP POS, PRP PRP, PRP PRP$, PRP RB, PRP RBR, PRP RP, PRP SYM, PRP TO, PRP UH, PRP VB, PRP VBD, PRP VBG, PRP VBN, PRP VBP, PRP VBZ, PRP WDT, PRP WP, PRP WRB, PRP ``, PRP$ ., PRP$ :, PRP$ CC, PRP$ CD, PRP$ DT, PRP$ FW, PRP$ HYPH, PRP$ IN, PRP$ JJ, PRP$ JJR, PRP$ JJS, PRP$ NN, PRP$ NNP, PRP$ NNPS, PRP$ NNS, PRP$ PRP, PRP$ PRP$, PRP$ RB, PRP$ RBR, PRP$ RBS, PRP$ UH, PRP$ VB, PRP$ VBD, PRP$ VBG, PRP$ VBN, PRP$ VBP, PRP$ VBZ, PRP$ XX, PRP$ ``, RB $, RB '', RB ,, RB -LRB-, RB -RRB-, RB ., RB :, RB ADD, RB CC, RB CD, RB DT, RB EX, RB HYPH, RB IN, RB JJ, RB JJR, RB JJS, RB MD, RB NFP, RB NN, RB NNP, RB NNPS, RB NNS, RB PDT, RB POS, RB PRP, RB PRP$, RB RB, RB RBR, RB RBS, RB RP, RB SYM, RB TO, RB UH, RB VB, RB VBD, RB VBG, RB VBN, RB VBP, RB VBZ, RB WDT, RB WP, RB WRB, RB ``, RBR ,, RBR ., RBR :, RBR CC, RBR DT, RBR HYPH, RBR IN, RBR JJ, RBR MD, RBR NFP, RBR NN, RBR NNP, RBR PRP, RBR RB, RBR TO, RBR VB, RBR VBD, RBR VBG, RBR VBN, RBR VBP, RBR VBZ, RBR WP$, RBR WRB, RBS ,, RBS ., RBS CD, RBS HYPH, RBS IN, RBS JJ, RBS NN, RBS PRP, RBS RB, RBS VB, RBS VBG, RBS VBN, RBS VBZ, RP $, RP '', RP ,, RP -LRB-, RP -RRB-, RP ., RP :, RP CC, RP CD, RP DT, RP IN, RP JJ, RP JJR, RP JJS, RP MD, RP NFP, RP NN, RP NNP, RP NNPS, RP NNS, RP PDT, RP PRP, RP PRP$, RP RB, RP RBR, RP TO, RP UH, RP VBG, RP VBZ, RP WDT, RP WP, RP WRB, RP ``, SYM CC, SYM CD, SYM DT, SYM JJ, SYM NN, SYM NNP, SYM NNS, SYM PRP, SYM RB, SYM VBG, SYM WRB, TO $, TO ,, TO ., TO :, TO CC, TO CD, TO DT, TO HYPH, TO IN, TO JJ, TO NN, TO NNP, TO RB, TO RBR, TO RBS, TO VB, TO VBG, TO VBN, TO WDT, TO WP, TO ``, UH '', UH ,, UH -RRB-, UH ., UH :, UH CC, UH CD, UH DT, UH EX, UH HYPH, UH IN, UH JJ, UH JJS, UH MD, UH NFP, UH NN, UH NNP, UH NNS, UH PRP, UH PRP$, UH RB, UH TO, UH UH, UH VB, UH VBD, UH VBN, UH VBP, UH VBZ, UH WP, UH WRB, VB $, VB '', VB ,, VB -LRB-, VB -RRB-, VB ., VB :, VB CC, VB CD, VB DT, VB EX, VB FW, VB HYPH, VB IN, VB JJ, VB JJR, VB JJS, VB MD, VB NFP, VB NN, VB NNP, VB NNPS, VB NNS, VB PDT, VB PRP, VB PRP$, VB RB, VB RBR, VB RP, VB TO, VB UH, VB VB, VB VBD, VB VBG, VB VBN, VB VBP, VB VBZ, VB WDT, VB WP, VB WP$, VB WRB, VB ``, VBD $, VBD ,, VBD -LRB-, VBD -RRB-, VBD ., VBD :, VBD CC, VBD CD, VBD DT, VBD EX, VBD FW, VBD IN, VBD JJ, VBD JJR, VBD JJS, VBD MD, VBD NFP, VBD NN, VBD NNP, VBD NNPS, VBD NNS, VBD PDT, VBD PRP, VBD PRP$, VBD RB, VBD RBR, VBD RP, VBD TO, VBD UH, VBD VB, VBD VBD, VBD VBG, VBD VBN, VBD VBP, VBD VBZ, VBD WDT, VBD WP, VBD WP$, VBD WRB, VBD XX, VBD ``, VBG $, VBG '', VBG ,, VBG -LRB-, VBG -RRB-, VBG ., VBG :, VBG CC, VBG CD, VBG DT, VBG EX, VBG HYPH, VBG IN, VBG JJ, VBG JJR, VBG JJS, VBG MD, VBG NFP, VBG NN, VBG NNP, VBG NNPS, VBG NNS, VBG PDT, VBG PRP, VBG PRP$, VBG RB, VBG RBR, VBG RBS, VBG RP, VBG TO, VBG UH, VBG VB, VBG VBD, VBG VBG, VBG VBN, VBG VBP, VBG VBZ, VBG WDT, VBG WP, VBG WRB, VBG ``, VBN $, VBN '', VBN ,, VBN -LRB-, VBN -RRB-, VBN ., VBN :, VBN CC, VBN CD, VBN DT, VBN EX, VBN HYPH, VBN IN, VBN JJ, VBN JJR, VBN JJS, VBN MD, VBN NFP, VBN NN, VBN NNP, VBN NNPS, VBN NNS, VBN PDT, VBN PRP, VBN PRP$, VBN RB, VBN RBR, VBN RP, VBN TO, VBN UH, VBN VB, VBN VBD, VBN VBG, VBN VBN, VBN VBP, VBN VBZ, VBN WDT, VBN WP, VBN WRB, VBN ``, VBP $, VBP '', VBP ,, VBP ., VBP :, VBP CC, VBP CD, VBP DT, VBP EX, VBP HYPH, VBP IN, VBP JJ, VBP JJR, VBP JJS, VBP MD, VBP NFP, VBP NN, VBP NNP, VBP NNPS, VBP NNS, VBP PDT, VBP POS, VBP PRP, VBP PRP$, VBP RB, VBP RBR, VBP RBS, VBP RP, VBP TO, VBP UH, VBP VB, VBP VBD, VBP VBG, VBP VBN, VBP VBP, VBP VBZ, VBP WDT, VBP WP, VBP WRB, VBP ``, VBZ $, VBZ '', VBZ ,, VBZ -LRB-, VBZ -RRB-, VBZ ., VBZ :, VBZ CC, VBZ CD, VBZ DT, VBZ EX, VBZ HYPH, VBZ IN, VBZ JJ, VBZ JJR, VBZ JJS, VBZ MD, VBZ NFP, VBZ NN, VBZ NNP, VBZ NNPS, VBZ NNS, VBZ PDT, VBZ PRP, VBZ PRP$, VBZ RB, VBZ RBR, VBZ RBS, VBZ RP, VBZ TO, VBZ UH, VBZ VB, VBZ VBD, VBZ VBG, VBZ VBN, VBZ VBP, VBZ VBZ, VBZ WDT, VBZ WP, VBZ WRB, VBZ ``, WDT ,, WDT ., WDT :, WDT CC, WDT CD, WDT DT, WDT EX, WDT IN, WDT JJ, WDT MD, WDT NN, WDT NNP, WDT NNPS, WDT NNS, WDT PRP, WDT PRP$, WDT RB, WDT TO, WDT VB, WDT VBD, WDT VBG, WDT VBN, WDT VBP, WDT VBZ, WDT ``, WP ,, WP ., WP :, WP CC, WP DT, WP EX, WP HYPH, WP IN, WP JJ, WP JJR, WP JJS, WP MD, WP NN, WP NNP, WP NNPS, WP NNS, WP PRP, WP PRP$, WP RB, WP TO, WP VBD, WP VBP, WP VBZ, WP WP, WP ``, WP$ CD, WP$ JJ, WP$ NN, WP$ NNS, WP$ VBG, WP$ VBN, WRB ,, WRB -LRB-, WRB ., WRB :, WRB CC, WRB CD, WRB DT, WRB EX, WRB HYPH, WRB IN, WRB JJ, WRB JJR, WRB JJS, WRB MD, WRB NN, WRB NNP, WRB NNPS, WRB NNS, WRB PDT, WRB PRP, WRB PRP$, WRB RB, WRB TO, WRB UH, WRB VB, WRB VBD, WRB VBG, WRB VBN, WRB VBP, WRB VBZ, WRB ``, XX -RRB-, XX CC, XX CD, XX DT, XX IN, XX JJ, XX NN, XX NNP, XX NNS, XX PRP, XX RB, XX VB, XX VBG, XX VBZ, XX WP, XX XX, XX ``, `` ,, `` ., `` :, `` CC, `` CD, `` DT, `` EX, `` FW, `` IN, `` JJ, `` JJR, `` JJS, `` MD, `` NFP, `` NN, `` NNP, `` NNPS, `` NNS, `` PDT, `` PRP, `` PRP$, `` RB, `` RBR, `` TO, `` UH, `` VB, `` VBD, `` VBG, `` VBN, `` VBP, `` VBZ, `` WDT, `` WP, `` WRB, `` XX, `` ``

selector SelectKBest

Features Out (120)

'' ., '' NNP, '' PRP, '' VBG, '' VBZ, , '', , CC, , IN, , PRP, , VBG, , ``, -RRB- ., . '', . CC, . IN, . PRP, . VBG, . ``, : '', : CC, : DT, : IN, : PRP, CC DT, CC IN, CC JJ, CC NN, CC NNS, CC PRP, CC TO, CC VB, CC VBZ, CD CD, DT IN, DT JJ, DT JJS, DT NN, DT NNP, DT NNS, DT VBZ, DT WP, IN DT, IN JJ, IN NN, IN NNP, IN NNPS, IN NNS, IN VBG, JJ NN, JJ NNS, JJR NN, MD RB, MD VB, NN '', NN ,, NN -LRB-, NN -RRB-, NN :, NN IN, NN MD, NN NNP, NN NNS, NN TO, NN WDT, NNP '', NNP -LRB-, NNP -RRB-, NNP MD, NNP NNP, NNP TO, NNP VBZ, NNPS VBP, NNS ,, NNS ., NNS :, NNS CC, NNS IN, NNS MD, NNS TO, NNS VBP, NNS WDT, NNS WP, PRP MD, PRP VBP, PRP VBZ, PRP$ JJ, PRP$ NNS, RB VB, RP IN, TO VB, UH UH, VB DT, VB IN, VB JJ, VB NN, VB NNS, VB PRP$, VBN DT, VBP DT, VBP IN, VBP JJ, VBP PRP, VBP RB, VBP TO, VBP VBG, VBP VBN, VBZ ,, VBZ IN, VBZ NNP, VBZ RP, WDT PRP, WDT VBP, WDT VBZ, WP VBP, `` DT, `` NN, `` NNP, `` PRP, `` RB, `` UH

textstats_vectors TextstatsTransformer

Features Out (12)

tokens_count, sentences_count, characters_count, monosyllabic_words_relfreq, polysyllabic_words_relfreq, unique_tokens_relfreq, average_characters_per_token, average_tokens_per_sentence, characters_proportion_letters, characters_proportion_uppercase, hapax_legomena_count, hapax_legomena_to_unique

selector SelectKBest

Features Out (6)

characters_count, polysyllabic_words_relfreq, unique_tokens_relfreq, average_tokens_per_sentence, characters_proportion_letters, hapax_legomena_to_unique

embeddings Model2VecEmbedder

Features Out (256)

emb_0, emb_1, emb_2, emb_3, emb_4, emb_5, emb_6, emb_7, emb_8, emb_9, emb_10, emb_11, emb_12, emb_13, emb_14, emb_15, emb_16, emb_17, emb_18, emb_19, emb_20, emb_21, emb_22, emb_23, emb_24, emb_25, emb_26, emb_27, emb_28, emb_29, emb_30, emb_31, emb_32, emb_33, emb_34, emb_35, emb_36, emb_37, emb_38, emb_39, emb_40, emb_41, emb_42, emb_43, emb_44, emb_45, emb_46, emb_47, emb_48, emb_49, emb_50, emb_51, emb_52, emb_53, emb_54, emb_55, emb_56, emb_57, emb_58, emb_59, emb_60, emb_61, emb_62, emb_63, emb_64, emb_65, emb_66, emb_67, emb_68, emb_69, emb_70, emb_71, emb_72, emb_73, emb_74, emb_75, emb_76, emb_77, emb_78, emb_79, emb_80, emb_81, emb_82, emb_83, emb_84, emb_85, emb_86, emb_87, emb_88, emb_89, emb_90, emb_91, emb_92, emb_93, emb_94, emb_95, emb_96, emb_97, emb_98, emb_99, emb_100, emb_101, emb_102, emb_103, emb_104, emb_105, emb_106, emb_107, emb_108, emb_109, emb_110, emb_111, emb_112, emb_113, emb_114, emb_115, emb_116, emb_117, emb_118, emb_119, emb_120, emb_121, emb_122, emb_123, emb_124, emb_125, emb_126, emb_127, emb_128, emb_129, emb_130, emb_131, emb_132, emb_133, emb_134, emb_135, emb_136, emb_137, emb_138, emb_139, emb_140, emb_141, emb_142, emb_143, emb_144, emb_145, emb_146, emb_147, emb_148, emb_149, emb_150, emb_151, emb_152, emb_153, emb_154, emb_155, emb_156, emb_157, emb_158, emb_159, emb_160, emb_161, emb_162, emb_163, emb_164, emb_165, emb_166, emb_167, emb_168, emb_169, emb_170, emb_171, emb_172, emb_173, emb_174, emb_175, emb_176, emb_177, emb_178, emb_179, emb_180, emb_181, emb_182, emb_183, emb_184, emb_185, emb_186, emb_187, emb_188, emb_189, emb_190, emb_191, emb_192, emb_193, emb_194, emb_195, emb_196, emb_197, emb_198, emb_199, emb_200, emb_201, emb_202, emb_203, emb_204, emb_205, emb_206, emb_207, emb_208, emb_209, emb_210, emb_211, emb_212, emb_213, emb_214, emb_215, emb_216, emb_217, emb_218, emb_219, emb_220, emb_221, emb_222, emb_223, emb_224, emb_225, emb_226, emb_227, emb_228, emb_229, emb_230, emb_231, emb_232, emb_233, emb_234, emb_235, emb_236, emb_237, emb_238, emb_239, emb_240, emb_241, emb_242, emb_243, emb_244, emb_245, emb_246, emb_247, emb_248, emb_249, emb_250, emb_251, emb_252, emb_253, emb_254, emb_255

scaler StandardScaler

Features Out (582)

tokens__!, tokens__", tokens__', tokens__'re, tokens__'ve, tokens__(, tokens__), tokens__*, tokens__--, tokens__.., tokens__..., tokens__...., tokens__....., tokens__?, tokens__Abel, tokens__Afghanistan, tokens__Alex, tokens__Alyx, tokens__America, tokens__American, tokens__Americans, tokens__And, tokens__But, tokens__Chase, tokens__China, tokens__Compton, tokens__Congress, tokens__Connelly, tokens__Daisy, tokens__Dave, tokens__Democrats, tokens__Go, tokens__God, tokens__He, tokens__Hiro, tokens__Iran, tokens__Iraq, tokens__Iraqi, tokens__Israel, tokens__Jonah, tokens__Keene, tokens__Our, tokens__Penny, tokens__Pobble, tokens__President, tokens__Qaeda, tokens__She, tokens__Standingwater, tokens__States, tokens__Thank, tokens__They, tokens__United, tokens__We, tokens__a.m., tokens__administration, tokens__ahead, tokens__allies, tokens__also, tokens__and, tokens__are, tokens__arrived, tokens__asks, tokens__at, tokens__back, tokens__billion, tokens__bit, tokens__bless, tokens__book, tokens__books, tokens__build, tokens__businesses, tokens__calories, tokens__can, tokens__care, tokens__challenges, tokens__children, tokens__cigarette, tokens__citizens, tokens__communities, tokens__companies, tokens__continue, tokens__countries, tokens__country, tokens__democracy, tokens__door, tokens__eat, tokens__economic, tokens__economy, tokens__education, tokens__election, tokens__energy, tokens__every, tokens__everybody, tokens__exercise, tokens__eyes, tokens__families, tokens__fat, tokens__federal, tokens__for, tokens__freedom, tokens__future, tokens__government, tokens__great, tokens__hand, tokens__have, tokens__he, tokens__head, tokens__health, tokens__help, tokens__her, tokens__him, tokens__his, tokens__history, tokens__hope, tokens__human, tokens__immigration, tokens__important, tokens__incredible, tokens__insurance, tokens__job, tokens__jobs, tokens__know, tokens__law, tokens__leaders, tokens__left, tokens__little, tokens__lives, tokens__looks, tokens__make, tokens__many, tokens__military, tokens__millions, tokens__must, tokens__my, tokens__myself, tokens__nation, tokens__national, tokens__nations, tokens__new, tokens__nods, tokens__novel, tokens__nuclear, tokens__of, tokens__off, tokens__opportunity, tokens__our, tokens__out, tokens__p.m., tokens__peace, tokens__people, tokens__plan, tokens__prosperity, tokens__protect, tokens__reform, tokens__regime, tokens__says, tokens__security, tokens__seems, tokens__she, tokens__shit, tokens__states, tokens__support, tokens__system, tokens__table, tokens__tax, tokens__terrorists, tokens__thank, tokens__that, tokens__their, tokens__then, tokens__these, tokens__they, tokens__this, tokens__those, tokens__though, tokens__to, tokens__together, tokens__towards, tokens__troops, tokens__turns, tokens__up, tokens__us, tokens__values, tokens__very, tokens__virus, tokens__walks, tokens__want, tokens__war, tokens__we, tokens__weapons, tokens__weight, tokens__who, tokens__will, tokens__work, tokens__workers, tokens__working, tokens__world, tokens__year, tokens__years, tokens__you, pos__'' ., pos__'' NNP, pos__'' PRP, pos__'' VBG, pos__'' VBZ, pos__, '', pos__, CC, pos__, IN, pos__, PRP, pos__, VBG, pos__, ``, pos__-RRB- ., pos__. 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classifier LogisticRegression

Features In (582)

The pipeline components allow you to extract a variety of feature types. Look at the example notebook for a more extensive example.

Warning: Running the next cell is not necessary unless you want to permanently delete the saved feature store.

os.remove('basic-introduction-store.sqlite')