|
15 | 15 | { |
16 | 16 | "cell_type": "code", |
17 | 17 | "execution_count": 1, |
| 18 | + "metadata": {}, |
| 19 | + "outputs": [ |
| 20 | + { |
| 21 | + "name": "stdout", |
| 22 | + "output_type": "stream", |
| 23 | + "text": [ |
| 24 | + "Collecting scikit-learn==0.21.3\n", |
| 25 | + " Using cached scikit_learn-0.21.3-cp36-cp36m-win_amd64.whl (5.9 MB)\n", |
| 26 | + "Collecting joblib>=0.11\n", |
| 27 | + " Using cached joblib-1.0.1-py3-none-any.whl (303 kB)\n", |
| 28 | + "Collecting numpy>=1.11.0\n", |
| 29 | + " Using cached numpy-1.19.5-cp36-cp36m-win_amd64.whl (13.2 MB)\n", |
| 30 | + "Collecting scipy>=0.17.0\n", |
| 31 | + " Using cached scipy-1.5.4-cp36-cp36m-win_amd64.whl (31.2 MB)\n", |
| 32 | + "Installing collected packages: numpy, scipy, joblib, scikit-learn\n", |
| 33 | + "Successfully installed joblib-1.0.1 numpy-1.19.5 scikit-learn-0.21.3 scipy-1.5.4\n" |
| 34 | + ] |
| 35 | + } |
| 36 | + ], |
| 37 | + "source": [ |
| 38 | + "# To install only the requirements of this notebook, uncomment the lines below and run this cell\n", |
| 39 | + "\n", |
| 40 | + "# ===========================\n", |
| 41 | + "\n", |
| 42 | + "!pip install scikit-learn==0.21.3\n", |
| 43 | + "\n", |
| 44 | + "# ===========================" |
| 45 | + ] |
| 46 | + }, |
| 47 | + { |
| 48 | + "cell_type": "code", |
| 49 | + "execution_count": 2, |
| 50 | + "metadata": {}, |
| 51 | + "outputs": [], |
| 52 | + "source": [ |
| 53 | + "# To install the requirements for the entire chapter, uncomment the lines below and run this cell\n", |
| 54 | + "\n", |
| 55 | + "# ===========================\n", |
| 56 | + "\n", |
| 57 | + "# try :\n", |
| 58 | + "# import google.colab\n", |
| 59 | + "# !curl https://raw.githubusercontent.com/practical-nlp/practical-nlp/master/Ch3/ch3-requirements.txt | xargs -n 1 -L 1 pip install\n", |
| 60 | + "# except ModuleNotFoundError :\n", |
| 61 | + "# !pip install -r \"ch3-requirements.txt\"\n", |
| 62 | + "\n", |
| 63 | + "# ===========================" |
| 64 | + ] |
| 65 | + }, |
| 66 | + { |
| 67 | + "cell_type": "code", |
| 68 | + "execution_count": 3, |
18 | 69 | "metadata": { |
19 | 70 | "colab": { |
20 | 71 | "base_uri": "https://localhost:8080/", |
|
31 | 82 | "['dog bites man', 'man bites dog', 'dog eats meat', 'man eats food']" |
32 | 83 | ] |
33 | 84 | }, |
34 | | - "execution_count": 1, |
| 85 | + "execution_count": 3, |
35 | 86 | "metadata": {}, |
36 | 87 | "output_type": "execute_result" |
37 | 88 | } |
|
56 | 107 | }, |
57 | 108 | { |
58 | 109 | "cell_type": "code", |
59 | | - "execution_count": 2, |
| 110 | + "execution_count": 4, |
60 | 111 | "metadata": { |
61 | 112 | "colab": { |
62 | 113 | "base_uri": "https://localhost:8080/", |
|
132 | 183 | "name": "python", |
133 | 184 | "nbconvert_exporter": "python", |
134 | 185 | "pygments_lexer": "ipython3", |
135 | | - "version": "3.6.7" |
| 186 | + "version": "3.6.13" |
136 | 187 | } |
137 | 188 | }, |
138 | 189 | "nbformat": 4, |
|
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