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#colab #deeplearning #machinglearning #imports #libraries

Deep learning essential libraries - imports

import transformers 
from transformers import BertModel, BertTokenizer
import torch

import copy
import pandas as pd
import numpy as np
import seaborn as sns
from pylab import rcParams
import matplotlib.pyplot as plt
from matplotlib import rc
from tqdm import tqdm

from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, classification_report

from collections import defaultdict
from textwrap import wrap

from torch import nn, optim
from torch.utils import data

%matplotlib inline
%config InlineBackend.figure_format='retina'

sns.set(style='whitegrid', palette='muted', font_scale=1.2)

HAPPY_COLORS_PALETTE = ["#01BEFE", "#FFDD00", "#FF7D00", "#FF006D", "#ADFF02", "#8F00FF"]

sns.set_palette(sns.color_palette(HAPPY_COLORS_PALETTE))

rcParams['figure.figsize'] = 6, 4

RANDOM_SEED = 42
np.random.seed(RANDOM_SEED)
torch.manual_seed(RANDOM_SEED)

##html ##css #javascript #fontawesome #bulma #boilerplate #imports

Boilerplate & Imports

<!doctype html>

<html lang="en">
<head>
  <meta charset="utf-8">
  <meta name="viewport" content="width=device-width, initial-scale=1">

  <title></title>

	/* CSS STYLESHEET */

	<link rel="stylesheet" href="styles.css">

	/* BULMA */

 	<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/bulma@0.9.3/css/bulma.min.css">
      
	/* FONTAWESOME */
      
	<script src="https://use.fontawesome.com/releases/v5.15.4/js/all.js" data-auto-	a11y="true"></script>


</head>

<body>
      
	/* JAVASCRIPT SCRIPTS /*
    
	<script src="script.js"></script>

</body>
</html>

Deep learning essential libraries - imports

Boilerplate & Imports

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