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View translation_char_example.py
import itertools | |
from torchtext.experimental.datasets import TextClassificationDataset | |
from torchtext.vocab import build_vocab_from_iterator | |
from torchtext.experimental.functional import sequential_transforms | |
from torchtext.experimental.datasets import IMDB | |
from torchtext.data.utils import get_tokenizer | |
from torch.nn.utils.rnn import pack_padded_sequence | |
def build_char_vocab(data, index, bow="<w>", eow="</w>"): |
View NLPM_pytorch.ipynb
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View quantile_normalize_example.py
# In []: | |
import pandas as pd | |
import numpy as np | |
import seaborn as sns | |
import matplotlib.pyplot as plt | |
import scipy as sp | |
A = np.random.normal(5, 1, 100) | |
B = np.random.normal(3, 1.5, 100) | |
C = np.random.normal(4, 2, 100) |
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View MCP_simulation.ipynb
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