|
17 | 17 | {
|
18 | 18 | "cell_type": "code",
|
19 | 19 | "execution_count": 1,
|
| 20 | + "metadata": {}, |
| 21 | + "outputs": [ |
| 22 | + { |
| 23 | + "name": "stdout", |
| 24 | + "output_type": "stream", |
| 25 | + "text": [ |
| 26 | + "Requirement already satisfied: scikit-learn==0.21.3 in c:\\users\\kumar apurv\\anaconda3\\envs\\ch3\\lib\\site-packages (0.21.3)\n", |
| 27 | + "Requirement already satisfied: numpy>=1.11.0 in c:\\users\\kumar apurv\\anaconda3\\envs\\ch3\\lib\\site-packages (from scikit-learn==0.21.3) (1.19.5)\n", |
| 28 | + "Requirement already satisfied: scipy>=0.17.0 in c:\\users\\kumar apurv\\anaconda3\\envs\\ch3\\lib\\site-packages (from scikit-learn==0.21.3) (1.5.4)\n", |
| 29 | + "Requirement already satisfied: joblib>=0.11 in c:\\users\\kumar apurv\\anaconda3\\envs\\ch3\\lib\\site-packages (from scikit-learn==0.21.3) (1.0.1)\n" |
| 30 | + ] |
| 31 | + } |
| 32 | + ], |
| 33 | + "source": [ |
| 34 | + "# To install only the requirements of this notebook, uncomment the lines below and run this cell\n", |
| 35 | + "\n", |
| 36 | + "# ===========================\n", |
| 37 | + "\n", |
| 38 | + "!pip install scikit-learn==0.21.3\n", |
| 39 | + "\n", |
| 40 | + "# ===========================" |
| 41 | + ] |
| 42 | + }, |
| 43 | + { |
| 44 | + "cell_type": "code", |
| 45 | + "execution_count": 2, |
| 46 | + "metadata": {}, |
| 47 | + "outputs": [], |
| 48 | + "source": [ |
| 49 | + "# To install the requirements for the entire chapter, uncomment the lines below and run this cell\n", |
| 50 | + "\n", |
| 51 | + "# ===========================\n", |
| 52 | + "\n", |
| 53 | + "# try :\n", |
| 54 | + "# import google.colab\n", |
| 55 | + "# !curl https://raw.githubusercontent.com/practical-nlp/practical-nlp/master/Ch3/ch3-requirements.txt | xargs -n 1 -L 1 pip install\n", |
| 56 | + "# except ModuleNotFoundError :\n", |
| 57 | + "# !pip install -r \"ch3-requirements.txt\"\n", |
| 58 | + "\n", |
| 59 | + "# ===========================" |
| 60 | + ] |
| 61 | + }, |
| 62 | + { |
| 63 | + "cell_type": "code", |
| 64 | + "execution_count": 3, |
20 | 65 | "metadata": {
|
21 | 66 | "colab": {
|
22 | 67 | "base_uri": "https://localhost:8080/",
|
|
33 | 78 | "['dog bites man', 'man bites dog', 'dog eats meat', 'man eats food']"
|
34 | 79 | ]
|
35 | 80 | },
|
36 |
| - "execution_count": 1, |
| 81 | + "execution_count": 3, |
37 | 82 | "metadata": {},
|
38 | 83 | "output_type": "execute_result"
|
39 | 84 | }
|
|
46 | 91 | },
|
47 | 92 | {
|
48 | 93 | "cell_type": "code",
|
49 |
| - "execution_count": 2, |
| 94 | + "execution_count": 4, |
50 | 95 | "metadata": {
|
51 | 96 | "colab": {
|
52 | 97 | "base_uri": "https://localhost:8080/",
|
|
126 | 171 | "name": "python",
|
127 | 172 | "nbconvert_exporter": "python",
|
128 | 173 | "pygments_lexer": "ipython3",
|
129 |
| - "version": "3.6.7" |
| 174 | + "version": "3.6.13" |
130 | 175 | }
|
131 | 176 | },
|
132 | 177 | "nbformat": 4,
|
|
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