robgon-art

import math
import torch
def predict(question, passage):
  sequence = tokenizer.encode_plus(question, passage, return_tensors="pt",
    max_length=512, truncation=True)['input_ids'].to(device)
  logits = model(sequence)[0]
  probabilities = torch.softmax(logits, dim=1).detach().cpu().tolist()[0]
  vector = logits.detach().cpu().tolist()[0]
  n = (vector[0]/2.75 + 1)/2
  y = (vector[1]/2.75 + 1)/2

robgon-art

Passage: The Magic 8-Ball is a plastic sphere, made to look like an eight-ball, that is used for
fortune-telling or seeking advice. It was invented in 1950 by Albert C. Carter and Abe Bookman
and is currently manufactured by Mattel. The user asks a yes–no question to the ball and then
turns it over to reveal an answer in a window on the ball

Question: Is the Magic 8-Ball a sphere?, Yes: 0.99, No: 0.01 Conf.: 0.98 
Question: Is the Magic 8-Ball a cube?, Yes: 0.11, No: 0.89 Conf.: 0.42 
Question: Was the Magic 8-Ball invented in 1940?, Yes: 0.04, No: 0.96 Conf.: 0.64
Question: Was the Magic 8-Ball invented in 1950?, Yes: 0.98, No: 0.02 Conf.: 0.77 
Question: Was the Magic 8-Ball invented by Carter and Bookman?, Yes: 0.97, No: 0.03 Conf.: 0.71

robgon-art

from tqdm import tqdm
batch_size = 8
epochs = 3
grad_acc_steps = 4
train_loss_values = []
dev_acc_values = []
for _ in tqdm(range(epochs), desc="Epoch"):
  # Training
  epoch_train_loss = 0
  model.train()

robgon-art

import pytextrank
import spacy
nlp = spacy.load("en_core_web_sm")
tr = pytextrank.TextRank()
nlp.add_pipe(tr.PipelineComponent, name="textrank", last=True)

question = "Can a computer beat a grandmaster chess player?"

query = ""
keywords = nlp(question.lower())

robgon-art

import wikipedia
print("Checking the Wikipedia.")
results = wikipedia.search(query, results = 3)
wiki_passage = ""
for r in results[1:]:
  try:
    s = wikipedia.summary(r)
  except:
    continue
  wiki_passage += s.strip() + " "

robgon-art

min_dist = float("inf")
pick = 0

if (conf < 0.5):
  map_conf =  1 +  conf * 19 / 2
  for i, a in enumerate(answers[:5]):
    c = a[1][1]
    distance = abs(map_conf-c)
    if distance < min_dist:
      min_dist = distance

robgon-art

conf = 0
yes = 0.5
passage = ""
source = "no source"

if (wiki_conf > nyt_conf and wiki_conf > boolq_conf):
  yes = wiki_yes
  conf = wiki_conf
  passage = wiki_passage
  source = "Wikipedia"

robgon-art

from gensim.models import Word2Vec
syllModel = Word2Vec.load(syll_model_path)
wordModel = Word2Vec.load(word_model_path)

import pyphen
dic = pyphen.Pyphen(lang='en_US')

poem = '''A plow, they say, to plow the snow.
They cannot mean to plant it, no–
Unless in bitterness to mock

robgon-art

import music21

def transpose_notes(notes, new_key):
  midi_stream = music21.stream.Stream(notes)
  key = midi_stream.analyze('key')
  interval = music21.interval.Interval(key.tonic, new_key.tonic)
  new_stream = midi_stream.transpose(interval)
  return new_stream.notes

# quantize the start times and note durations

robgon-art

  length_song = len(lyrics)
  cond = []
 
  for i in range(20):
    if i < length_song:
      syll2Vec = syllModel.wv[lyrics[i][0]]
      word2Vec = wordModel.wv[lyrics[i][1]]
      cond.append(np.concatenate((syll2Vec, word2Vec)))
    else:
      cond.append(np.concatenate((syll2Vec, word2Vec)))