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✨ seq2seq Machine translation pytorch
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| "name": "#%% md\n" | ||
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| "source": [ | ||
| "# Machine translation Pytorch" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "outputs": [ | ||
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| }, | ||
| "metadata": {}, | ||
| "output_type": "display_data" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "import numpy as np\n", | ||
| "import pandas as pd\n", | ||
| "import csv\n", | ||
| "import re\n", | ||
| "import imblearn\n", | ||
| "import torch\n", | ||
| "from nltk.corpus import stopwords\n", | ||
| "from nltk.stem import WordNetLemmatizer\n", | ||
| "from gensim.parsing.porter import PorterStemmer\n", | ||
| "from gensim.utils import tokenize\n", | ||
| "from gensim.utils import simple_preprocess\n", | ||
| "from gensim.parsing.preprocessing import remove_stopwords\n", | ||
| "import torch.nn as nn\n", | ||
| "import torch.nn.functional as F\n", | ||
| "import torch.optim as optim\n", | ||
| "from torch.autograd import Variable\n", | ||
| "import torch\n", | ||
| "from torchtext.legacy import data\n", | ||
| "# from torchtext import data\n", | ||
| "import matplotlib.pyplot as plt\n", | ||
| "import seaborn as sb\n", | ||
| "from sklearn.metrics import roc_curve,auc\n", | ||
| "from numpy import interp\n", | ||
| "from itertools import cycle\n", | ||
| "from sklearn.metrics import accuracy_score\n", | ||
| "from sklearn.metrics import confusion_matrix\n", | ||
| "from sklearn.model_selection import train_test_split\n", | ||
| "from sklearn.metrics import classification_report\n", | ||
| "from bokeh.models import ColumnDataSource, LabelSet\n", | ||
| "from bokeh.plotting import figure, show, output_file\n", | ||
| "from collections import Counter\n", | ||
| "from functools import reduce\n", | ||
| "# ! pip install captum bokeh spacy emot parameter-sherpa\n", | ||
| "import sherpa\n", | ||
| "import captum\n", | ||
| "import spacy\n", | ||
| "from captum.attr import LayerIntegratedGradients, TokenReferenceBase, visualization, IntegratedGradients, LayerConductance\n", | ||
| "from captum.attr import visualization as viz\n", | ||
| "from captum.attr import configure_interpretable_embedding_layer, remove_interpretable_embedding_layer\n", | ||
| "import emot\n", | ||
| "from bokeh.io import output_notebook\n", | ||
| "output_notebook()\n", | ||
| "# ! python -m spacy download en_core_web_sm\n", | ||
| "nlp = spacy.load('en')\n", | ||
| "%matplotlib inline" | ||
| ], | ||
| "metadata": { | ||
| "collapsed": false, | ||
| "pycharm": { | ||
| "name": "#%%\n" | ||
| } | ||
| } | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "outputs": [], | ||
| "source": [], | ||
| "metadata": { | ||
| "collapsed": false, | ||
| "pycharm": { | ||
| "name": "#%%\n" | ||
| } | ||
| } | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "Python 3", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 2 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython2", | ||
| "version": "2.7.6" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 0 | ||
| } |