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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Kushagratandon12/Introduction_to_tensorflow/blob/master/Natural_Launguage_Processing_Notes.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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      "metadata": {
        "id": "RInro07I0R_c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
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      "metadata": {
        "id": "GiTlooiJgK9T",
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        "colab": {
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          "height": 35
        },
        "outputId": "3fdd774b-dfbb-415a-a020-9c6588daa172"
      },
      "source": [
        "print(tf.__version__)\n",
        "!pip install -q tensorflow-datasets"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.3.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XQmW2cXygsq2",
        "colab_type": "text"
      },
      "source": [
        "# Learning Natural Language Processing (NLP)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3nhl30oz0m7-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sentences = [\n",
        "             'Hi My Name Is Kushagra',\n",
        "             'Hi My Name Is Samarth',\n",
        "             'I Love My Dog',\n",
        "             'You Love Dog!'\n",
        "]"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xqt-9yg30w4_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6262e50e-ed10-4159-cab4-440ca9c36150"
      },
      "source": [
        "#creating an instance for the tokenizer \n",
        "tokenizer = Tokenizer(num_words=100,oov_token=\"<OOV>\")\n",
        "print(tokenizer)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<keras_preprocessing.text.Tokenizer object at 0x7f3bfd8c9470>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7O5JdyKB033D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "dca92f86-90ad-4774-d238-4b816807d1fd"
      },
      "source": [
        "tokenizer.fit_on_texts(sentences)\n",
        "words_index = tokenizer.word_index\n",
        "print(words_index)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'<OOV>': 1, 'my': 2, 'hi': 3, 'name': 4, 'is': 5, 'love': 6, 'dog': 7, 'kushagra': 8, 'samarth': 9, 'i': 10, 'you': 11}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IRrvif7i1DKz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b3f1738c-7868-4eef-8a1d-3ab40b52a560"
      },
      "source": [
        "sequence = tokenizer.texts_to_sequences(sentences)\n",
        "print(sequence)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[3, 2, 4, 5, 8], [3, 2, 4, 5, 9], [10, 6, 2, 7], [11, 6, 7]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YuIUeeyW32fZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "340bb6e8-4860-4192-ea42-767d5b637456"
      },
      "source": [
        "padding = pad_sequences(sequence,maxlen=5)\n",
        "print(padding)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 3  2  4  5  8]\n",
            " [ 3  2  4  5  9]\n",
            " [ 0 10  6  2  7]\n",
            " [ 0  0 11  6  7]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ufPpAmZwg3_h",
        "colab_type": "text"
      },
      "source": [
        "# IMDB MOVIE REVIEW PROJECT\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iEr8m8XPg689",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow_datasets as tfds"
      ],
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q6RY2oHrhAJW",
        "colab_type": "code",
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          "referenced_widgets": [
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          ]
        },
        "outputId": "8d3ae9b7-4d5f-45e4-8814-fbc3ecae2061"
      },
      "source": [
        "imdb , info = tfds.load(\"imdb_reviews\" , with_info=True,as_supervised=True)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading and preparing dataset imdb_reviews/plain_text/1.0.0 (download: 80.23 MiB, generated: Unknown size, total: 80.23 MiB) to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0...\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "19638af18a6f40cf854e53fb26992a9e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Completed...', max=1.0, style=Progre…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Size...', max=1.0, style=ProgressSty…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8a65bf8de46c47b886213db15eacf3bb",
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteB1YUFV/imdb_reviews-train.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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              "HBox(children=(FloatProgress(value=0.0, max=25000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1068344eaf0c47729f3ec54ee5f62d04",
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              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteB1YUFV/imdb_reviews-test.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "49635fc1045d419581168e3ac110cde4",
              "version_minor": 0,
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              "HBox(children=(FloatProgress(value=0.0, max=25000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8ae044c46f67421da22b5a997256984a",
              "version_minor": 0,
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            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteB1YUFV/imdb_reviews-unsupervised.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "70b94ff6cead45e5bcb1bb6d5160dee1",
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              "HBox(children=(FloatProgress(value=0.0, max=50000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDataset imdb_reviews downloaded and prepared to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n",
            "\r"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jNA6NLtehaHv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "e31ca119-e33c-4c85-9354-d8ae54cafaff"
      },
      "source": [
        "test_data , train_data = imdb['test'] , imdb['train']\n",
        "print(type(test_data))\n",
        "print(type(train_data))"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'tensorflow.python.data.ops.dataset_ops.DatasetV1Adapter'>\n",
            "<class 'tensorflow.python.data.ops.dataset_ops.DatasetV1Adapter'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "osmbT1DeiGki",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "893df16c-245f-48cb-85f8-fe4388468326"
      },
      "source": [
        "training_sentences = []\n",
        "training_labels = []\n",
        "for s,l in train_data:\n",
        "  training_sentences.append(str(s.numpy()))\n",
        "  training_labels.append(l.numpy())\n",
        "\n",
        "print(type(training_sentences))"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'list'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gbjo_k-ci1Rl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "testing_sentences=[]\n",
        "testing_labels=[]\n",
        "for s,l in test_data:\n",
        "  testing_sentences.append(str(s.numpy()))\n",
        "  testing_labels.append(l.numpy())"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1-LCbDYSjkC_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "training_labels_final =np.array(training_labels)\n",
        "testing_labels_final = np.array(testing_labels) "
      ],
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7-3Tyl2nmT7e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# tokenizer = Tokenizer(num_words=10000,oov_token=\"<OOV>\")\n",
        "# tokenizer.fit_on_texts(training_sentences)\n",
        "# words_index = tokenizer.word_index\n",
        "# sequence = tokenizer.texts_to_sequences(training_sentences)\n",
        "# padded = pad_sequences(sequence , padding='post',maxlen=120)\n",
        "# print(words_index)"
      ],
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XJqv80XZzFQA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vocab_size = 10000\n",
        "embedding_dim = 16\n",
        "max_length = 120\n",
        "trunc_type='post'\n",
        "oov_tok = \"<OOV>\"\n",
        "\n",
        "tokenizer = Tokenizer(num_words = vocab_size, oov_token=oov_tok)\n",
        "tokenizer.fit_on_texts(training_sentences)\n",
        "word_index = tokenizer.word_index\n",
        "sequences = tokenizer.texts_to_sequences(training_sentences)\n",
        "padded = pad_sequences(sequences,maxlen=max_length, truncating=trunc_type)\n",
        "\n",
        "testing_sequences = tokenizer.texts_to_sequences(testing_sentences)\n",
        "testing_padded = pad_sequences(testing_sequences,maxlen=max_length)\n"
      ],
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "svRzRPjZzTem",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\n",
        "\n",
        "def decode_review(text):\n",
        "    return ' '.join([reverse_word_index.get(i, '?') for i in text])"
      ],
      "execution_count": 110,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-AYWJit5nfBT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "outputId": "06006a4c-f313-44fb-ee10-23c8f2c61aa3"
      },
      "source": [
        "model = tf.keras.Sequential([    \n",
        "                             tf.keras.layers.Embedding(10000,16,input_length=120),\n",
        "                             tf.keras.layers.GlobalAveragePooling1D(),\n",
        "                             tf.keras.layers.Dense(6,activation='relu'),\n",
        "                             tf.keras.layers.Dense(1,activation='sigmoid')\n",
        "                             \n",
        "])\n",
        "model.summary()\n",
        "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_5 (Embedding)      (None, 120, 16)           160000    \n",
            "_________________________________________________________________\n",
            "global_average_pooling1d_2 ( (None, 16)                0         \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 6)                 102       \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 1)                 7         \n",
            "=================================================================\n",
            "Total params: 160,109\n",
            "Trainable params: 160,109\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FwFrWHGmuR52",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "729d1277-6dce-4d5d-e1f7-9e73345b5f14"
      },
      "source": [
        "model.fit(padded,\n",
        "          training_labels_final,\n",
        "          epochs=16,\n",
        "          validation_data=(testing_padded,testing_labels_final))"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/16\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.5327 - accuracy: 0.7632 - val_loss: 0.3708 - val_accuracy: 0.8455\n",
            "Epoch 2/16\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.3011 - accuracy: 0.8790 - val_loss: 0.3201 - val_accuracy: 0.8628\n",
            "Epoch 3/16\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.2443 - accuracy: 0.9021 - val_loss: 0.3188 - val_accuracy: 0.8624\n",
            "Epoch 4/16\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.2115 - accuracy: 0.9198 - val_loss: 0.3327 - val_accuracy: 0.8570\n",
            "Epoch 5/16\n",
            "782/782 [==============================] - 3s 4ms/step - loss: 0.1873 - accuracy: 0.9310 - val_loss: 0.3539 - val_accuracy: 0.8521\n",
            "Epoch 6/16\n",
            "782/782 [==============================] - 3s 4ms/step - loss: 0.1676 - accuracy: 0.9398 - val_loss: 0.3734 - val_accuracy: 0.8490\n",
            "Epoch 7/16\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.1519 - accuracy: 0.9465 - val_loss: 0.4043 - val_accuracy: 0.8457\n",
            "Epoch 8/16\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.1376 - accuracy: 0.9528 - val_loss: 0.4317 - val_accuracy: 0.8406\n",
            "Epoch 9/16\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.1253 - accuracy: 0.9578 - val_loss: 0.4665 - val_accuracy: 0.8365\n",
            "Epoch 10/16\n",
            "782/782 [==============================] - 4s 4ms/step - loss: 0.1139 - accuracy: 0.9632 - val_loss: 0.5003 - val_accuracy: 0.8350\n",
            "Epoch 11/16\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.1036 - accuracy: 0.9670 - val_loss: 0.5416 - val_accuracy: 0.8291\n",
            "Epoch 12/16\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.0940 - accuracy: 0.9708 - val_loss: 0.5822 - val_accuracy: 0.8307\n",
            "Epoch 13/16\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.0849 - accuracy: 0.9752 - val_loss: 0.6343 - val_accuracy: 0.8248\n",
            "Epoch 14/16\n",
            "782/782 [==============================] - 3s 4ms/step - loss: 0.0761 - accuracy: 0.9786 - val_loss: 0.6727 - val_accuracy: 0.8258\n",
            "Epoch 15/16\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.0676 - accuracy: 0.9826 - val_loss: 0.7163 - val_accuracy: 0.8228\n",
            "Epoch 16/16\n",
            "782/782 [==============================] - 4s 5ms/step - loss: 0.0612 - accuracy: 0.9840 - val_loss: 0.7687 - val_accuracy: 0.8191\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f3bf44cdd68>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WLAbeMlwx2JN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "18b89da7-1c2d-4ce4-a605-b938bd315c35"
      },
      "source": [
        "e = model.layers[0]\n",
        "weights = e.get_weights()[0]\n",
        "print(weights.shape)"
      ],
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000, 16)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A_MPpQ6kyyF9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "f55948d7-0ade-47f7-f3a8-f696a58ee34a"
      },
      "source": [
        "import io\n",
        "out_v = io.open('vecs.tsv', 'w', encoding='utf-8')\n",
        "out_m = io.open('meta.tsv', 'w', encoding='utf-8')\n",
        "for word_num in range(1, vocab_size):\n",
        "  word = reverse_word_index[word_num]\n",
        "  embeddings = weights[word_num]\n",
        "  out_m.write(word + \"\\n\")\n",
        "  out_v.write('\\t'.join([str(x) for x in embeddings]) + \"\\n\")\n",
        "out_v.close()\n",
        "out_m.close()"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[-0.09067859 -0.06638046 -0.1156228   0.06845208 -0.19336942 -0.00304176\n",
            "  0.20963508  0.06484628 -0.03394631 -0.09478562  0.08967552  0.07388046\n",
            " -0.02438696  0.10826803 -0.00640544 -0.10164997]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f0r3vg5lzgIM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "9e2b9634-5af4-4192-9c30-72a6fdd0b85f"
      },
      "source": [
        "try:\n",
        "  from google.colab import files\n",
        "except ImportError:\n",
        "  pass\n",
        "else:\n",
        "  files.download('vecs.tsv')\n",
        "  files.download('meta.tsv')"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_ffa36759-cdb6-4f92-a558-39e4eb267cf7\", \"vecs.tsv\", 1846383)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_c441c40d-c626-4f13-af17-74bc14536e30\", \"meta.tsv\", 75846)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}