Subword_Tokenization.ipynb

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            "910e4e9fc73f44f4ab261ce4e7fdc67d",
            "bd9fb96bc92f4b6b93f80ec9b3a46d19",
            "2d6c35ad6d1446f1b5423447226a0ff7",
            "e87da0c3f87544b9b042786e60169b08",
            "bae4f1a2c5f34745b649aefcb47f74d7",
            "c0460ebf34224c198c146a38022964f6",
            "2a223dc8c8bd4b28a8948f737b748d74"
          ]
        }
      },
      "source": [
        "import tensorflow_datasets as tfds\n",
        "imdb, info = tfds.load(\"imdb_reviews/subwords8k\", with_info=True, as_supervised=True)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading and preparing dataset imdb_reviews/subwords8k/1.0.0 (download: 80.23 MiB, generated: Unknown size, total: 80.23 MiB) to /root/tensorflow_datasets/imdb_reviews/subwords8k/1.0.0...\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "156c1144a69e4dac987af99e195d1e19",
              "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": {
              "model_id": "2a946547422e41738ecb3e053b759316",
              "version_minor": 0,
              "version_major": 2
            },
            "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": "2970fe5986b74aae8cf3d1c0ca3e1d21",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "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/subwords8k/1.0.0.incomplete8QJE7U/imdb_reviews-train.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "ce2f50cda7aa439a8429a2d2b3caef60",
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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": "c491e5c7481546c7b32dd0e9253b44e7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "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/subwords8k/1.0.0.incomplete8QJE7U/imdb_reviews-test.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a32717aa646d402f9bec8f42416f4e73",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "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": "04f4a66ec7064a1e857d61dc68a5591c",
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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/subwords8k/1.0.0.incomplete8QJE7U/imdb_reviews-unsupervised.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9b3d396d95b643e88738fcedc586d56e",
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            },
            "text/plain": [
              "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/subwords8k/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n",
            "\r"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ynvGGj4s0vSf"
      },
      "source": [
        "train_data, test_data = imdb['train'], imdb['test']"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4qvrXba10vru"
      },
      "source": [
        "tokenizer = info.features['text'].encoder"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eOo5HE3o03jD",
        "outputId": "98f24282-1118-4d5f-ba70-81f72b23a289",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print(tokenizer.subwords)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['the_', ', ', '. ', 'a_', 'and_', 'of_', 'to_', 's_', 'is_', 'br', 'in_', 'I_', 'that_', 'this_', 'it_', ' /><', ' />', 'was_', 'The_', 'as_', 't_', 'with_', 'for_', '.<', 'on_', 'but_', 'movie_', ' (', 'are_', 'his_', 'have_', 'film_', 'not_', 'ing_', 'be_', 'ed_', 'you_', ' \"', 'it', 'd_', 'an_', 'he_', 'by_', 'at_', 'one_', 'who_', 'y_', 'from_', 'e_', 'or_', 'all_', 'like_', 'they_', '\" ', 'so_', 'just_', 'has_', ') ', 'her_', 'about_', 'out_', 'This_', 'some_', 'ly_', 'movie', 'film', 'very_', 'more_', 'It_', 'would_', 'what_', 'when_', 'which_', 'good_', 'if_', 'up_', 'only_', 'even_', 'their_', 'had_', 'really_', 'my_', 'can_', 'no_', 'were_', 'see_', 'she_', '? ', 'than_', '! ', 'there_', 'get_', 'been_', 'into_', ' - ', 'will_', 'much_', 'story_', 'because_', 'ing', 'time_', 'n_', 'we_', 'ed', 'me_', ': ', 'most_', 'other_', 'don', 'do_', 'm_', 'es_', 'how_', 'also_', 'make_', 'its_', 'could_', 'first_', 'any_', \"' \", 'people_', 'great_', 've_', 'ly', 'er_', 'made_', 'r_', 'But_', 'think_', \" '\", 'i_', 'bad_', 'A_', 'And_', 'It', 'on', '; ', 'him_', 'being_', 'never_', 'way_', 'that', 'many_', 'then_', 'where_', 'two_', 'In_', 'after_', 'too_', 'little_', 'you', '), ', 'well_', 'ng_', 'your_', 'If_', 'l_', '). ', 'does_', 'ever_', 'them_', 'did_', 'watch_', 'know_', 'seen_', 'time', 'er', 'character_', 'over_', 'characters_', 'movies_', 'man_', 'There_', 'love_', 'best_', 'still_', 'off_', 'such_', 'in', 'should_', 'the', 're_', 'He_', 'plot_', 'films_', 'go_', 'these_', 'acting_', 'doesn', 'es', 'show_', 'through_', 'better_', 'al_', 'something_', 'didn', 'back_', 'those_', 'us_', 'less_', '...', 'say_', 'is', 'one', 'makes_', 'and', 'can', 'all', 'ion_', 'find_', 'scene_', 'old_', 'real_', 'few_', 'going_', 'well', 'actually_', 'watching_', 'life_', 'me', '. <', 'o_', 'man', 'there', 'scenes_', 'same_', 'he', 'end_', 'this', '... ', 'k_', 'while_', 'thing_', 'of', 'look_', 'quite_', 'out', 'lot_', 'want_', 'why_', 'seems_', 'every_', 'll_', 'pretty_', 'got_', 'able_', 'nothing_', 'good', 'As_', 'story', ' & ', 'another_', 'take_', 'to', 'years_', 'between_', 'give_', 'am_', 'work_', 'isn', 'part_', 'before_', 'actors_', 'may_', 'gets_', 'young_', 'down_', 'around_', 'ng', 'thought_', 'though_', 'end', 'without_', 'What_', 'They_', 'things_', 'life', 'always_', 'must_', 'cast_', 'almost_', 'h_', '10', 'saw_', 'own_', 'here', 'bit_', 'come_', 'both_', 'might_', 'g_', 'whole_', 'new_', 'director_', 'them', 'horror_', 'ce', 'You_', 'least_', 'bad', 'big_', 'enough_', 'him', 'feel_', 'probably_', 'up', 'here_', 'making_', 'long_', 'her', 'st_', 'kind_', '--', 'original_', 'fact_', 'rather_', 'or', 'far_', 'nt_', 'played_', 'found_', 'last_', 'movies', 'When_', 'so', '\", ', 'comes_', 'action_', 'She_', 've', 'our_', 'anything_', 'funny_', 'ion', 'right_', 'way', 'trying_', 'now_', 'ous_', 'each_', 'done_', 'since_', 'ic_', 'point_', '\". ', 'wasn', 'interesting_', 'c_', 'worst_', 'te_', 'le_', 'ble_', 'ty_', 'looks_', 'show', 'put_', 'looking_', 'especially_', 'believe_', 'en_', 'goes_', 'over', 'ce_', 'p_', 'films', 'hard_', 'main_', 'be', 'having_', 'ry', 'TV_', 'worth_', 'One_', 'do', 'al', 're', 'again', 'series_', 'takes_', 'guy_', 'family_', 'seem_', 'plays_', 'role_', 'away_', 'world_', 'My_', 'character', ', \"', 'performance_', '2_', 'So_', 'watched_', 'John_', 'th_', 'plot', 'script_', 'For_', 'sure_', 'characters', 'set_', 'different_', 'minutes_', 'All_', 'American_', 'anyone_', 'Not_', 'music_', 'ry_', 'shows_', 'too', 'son_', 'en', 'day_', 'use_', 'someone_', 'for', 'woman_', 'yet_', '.\" ', 'during_', 'she', 'ro', '- ', 'times_', 'left_', 'used_', 'le', 'three_', 'play_', 'work', 'ness_', 'We_', 'girl_', 'comedy_', 'ment_', 'an', 'simply_', 'off', 'ies_', 'funny', 'ne', 'acting', 'That_', 'fun_', 'completely_', 'st', 'seeing_', 'us', 'te', 'special_', 'ation_', 'as', 'ive_', 'ful_', 'read_', 'reason_', 'co', 'need_', 'sa', 'true_', 'ted_', 'like', 'ck', 'place_', 'they', '10_', 'However', 'until_', 'rest_', 'sense_', 'ity_', 'everything_', 'people', 'nt', 'ending_', 'again_', 'ers_', 'given_', 'idea_', 'let_', 'nice_', 'help_', 'no', 'truly_', 'beautiful_', 'ter', 'ck_', 'version_', 'try_', 'came_', 'Even_', 'DVD_', 'se', 'mis', 'scene', 'job_', 'ting_', 'Me', 'At_', 'who', 'money_', 'ment', 'ch', 'recommend_', 'was', 'once_', 'getting_', 'tell_', 'de_', 'gives_', 'not', 'Lo', 'we', 'son', 'shot_', 'second_', 'After_', 'To_', 'high_', 'screen_', ' -- ', 'keep_', 'felt_', 'with', 'great', 'everyone_', 'although_', 'poor_', 'el', 'half_', 'playing_', 'couple_', 'now', 'ble', 'excellent_', 'enjoy_', 'couldn', 'x_', 'ne_', ',\" ', 'ie_', 'go', 'become_', 'less', 'himself_', 'supposed_', 'won', 'understand_', 'seen', 'ally_', 'THE_', 'se_', 'actor_', 'ts_', 'small_', 'line_', 'na', 'audience_', 'fan_', 'et', 'world', 'entire_', 'said_', 'at', '3_', 'scenes', 'rs_', 'full_', 'year_', 'men_', 'ke', 'doing_', 'went_', 'director', 'back', 'early_', 'Hollywood_', 'start_', 'liked_', 'against_', 'remember_', 'love', 'He', 'along_', 'ic', 'His_', 'wife_', 'effects_', 'together_', 'ch_', 'Ra', 'ty', 'maybe_', 'age', 'S_', 'While_', 'often_', 'sort_', 'definitely_', 'No', 'script', 'times', 'absolutely_', 'book_', 'day', 'human_', 'There', 'top_', 'ta', 'becomes_', 'piece_', 'waste_', 'seemed_', 'down', '5_', 'later_', 'rs', 'ja', 'certainly_', 'budget_', 'th', 'nce_', '200', '. 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', '80s_', '\"), ', 'wol', 'weekend', 'vampires', 'underst', 'tial_', 'terrorist', 'strength_', 'starre', 'soldier_', 'snow', 'sity', 'ruin_', 'retar', 'resu', 'required', 'recommended', 'ques', 'propo', 'presents_', 'perm', 'overt', 'olds', 'occas', 'nn_', 'nen', 'nei', 'mail', 'lost', 'lion', 'libr', 'inner_', 'headed', 'happy', 'guest', 'govern', 'friendly', 'explains', 'ens_', 'effectively', 'draw_', 'downright', 'dete', 'dde', 'dare', 'cring', 'courag', 'conspi', 'comedie', 'claims_', 'cide', 'chas', 'captivat', 'bite', 'bare', 'author_', 'addition', 'Vid', 'Rh', 'Oliv', 'Nata', 'Mexican', 'Keaton_', 'Iron', 'Barb', 'ALL_', '12', '!), ', 'worthwhile', 'weake', 'ung', 'understood_', 'unbelievable', 'superf', 'stolen', 'stereotypic', 'spoiler', 'sight', 'scares', 'rut', 'remove', 'remotely_', 'releva', 'prese', 'poke', 'ndou', 'mbla', 'lucky_', 'lling_', 'legendary', 'imagery', 'humou', 'hug', 'hired', 'heck', 'guilty', 'extras', 'expected', 'everywhere', 'dry_', 'drea', 'directed', 'dimensional_', 'ddi', 'dden', 'communica', 'cham', 'buddy', 'bank_', 'azi', 'algi', 'adventures', 'accurate_', 'accompan', 'Thom', 'Still_', 'Someone', 'Serious', 'SU', 'Phill', 'Perso', 'Patrick_', 'Lei', 'Jus', 'Gho', 'Get_', 'Freeman', 'Especially_', '?).', '...\"']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ziAIz4o031p",
        "outputId": "2f37e28f-c3f3-4635-90d7-a7d6a4703b89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "sample_string = 'TensorFlow, from basics to mastery'\n",
        "\n",
        "tokenized_string = tokenizer.encode(sample_string)\n",
        "print ('Tokenized string is {}'.format(tokenized_string))\n",
        "\n",
        "original_string = tokenizer.decode(tokenized_string)\n",
        "print ('The original string: {}'.format(original_string))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tokenized string is [6307, 2327, 4043, 2120, 2, 48, 4249, 4429, 7, 2652, 8050]\n",
            "The original string: TensorFlow, from basics to mastery\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xbqyKEIM07mc",
        "outputId": "d3ba5e55-3e78-4889-c9d9-b4823bb422d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        }
      },
      "source": [
        "for ts in tokenized_string:\n",
        "  print ('{} ----> {}'.format(ts, tokenizer.decode([ts])))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6307 ----> Ten\n",
            "2327 ----> sor\n",
            "4043 ----> Fl\n",
            "2120 ----> ow\n",
            "2 ----> , \n",
            "48 ----> from \n",
            "4249 ----> basi\n",
            "4429 ----> cs \n",
            "7 ----> to \n",
            "2652 ----> master\n",
            "8050 ----> y\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DZxuc94v07w6"
      },
      "source": [
        "BUFFER_SIZE = 10000\n",
        "BATCH_SIZE = 64\n",
        "\n",
        "train_dataset = train_data.shuffle(BUFFER_SIZE)\n",
        "train_dataset = train_dataset.padded_batch(BATCH_SIZE, tf.compat.v1.data.get_output_shapes(train_dataset))\n",
        "test_dataset = test_data.padded_batch(BATCH_SIZE, tf.compat.v1.data.get_output_shapes(test_data))"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fz0hvn1X07zR",
        "outputId": "17380fd2-0e59-4890-b26e-c939264d08ba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "embedding_dim = 64\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(tokenizer.vocab_size, embedding_dim),\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()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, None, 64)          523840    \n",
            "_________________________________________________________________\n",
            "global_average_pooling1d (Gl (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 6)                 390       \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 7         \n",
            "=================================================================\n",
            "Total params: 524,237\n",
            "Trainable params: 524,237\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kenWZ2k01D4f",
        "outputId": "bcd2303f-97c3-45fe-cd31-abcaa2e7710e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        }
      },
      "source": [
        "num_epochs = 10\n",
        "\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(train_dataset, epochs=num_epochs, validation_data=test_dataset)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "391/391 [==============================] - 11s 27ms/step - loss: 0.6803 - accuracy: 0.6214 - val_loss: 0.6397 - val_accuracy: 0.7514\n",
            "Epoch 2/10\n",
            "391/391 [==============================] - 10s 27ms/step - loss: 0.5398 - accuracy: 0.7916 - val_loss: 0.4712 - val_accuracy: 0.7997\n",
            "Epoch 3/10\n",
            "391/391 [==============================] - 11s 27ms/step - loss: 0.3862 - accuracy: 0.8633 - val_loss: 0.3760 - val_accuracy: 0.8574\n",
            "Epoch 4/10\n",
            "391/391 [==============================] - 11s 27ms/step - loss: 0.3111 - accuracy: 0.8887 - val_loss: 0.3344 - val_accuracy: 0.8732\n",
            "Epoch 5/10\n",
            "391/391 [==============================] - 10s 27ms/step - loss: 0.2688 - accuracy: 0.9042 - val_loss: 0.3172 - val_accuracy: 0.8775\n",
            "Epoch 6/10\n",
            "391/391 [==============================] - 11s 27ms/step - loss: 0.2425 - accuracy: 0.9138 - val_loss: 0.3152 - val_accuracy: 0.8749\n",
            "Epoch 7/10\n",
            "391/391 [==============================] - 10s 27ms/step - loss: 0.2215 - accuracy: 0.9222 - val_loss: 0.3078 - val_accuracy: 0.8802\n",
            "Epoch 8/10\n",
            "391/391 [==============================] - 11s 27ms/step - loss: 0.2066 - accuracy: 0.9275 - val_loss: 0.3117 - val_accuracy: 0.8780\n",
            "Epoch 9/10\n",
            "391/391 [==============================] - 11s 27ms/step - loss: 0.1925 - accuracy: 0.9309 - val_loss: 0.3102 - val_accuracy: 0.8812\n",
            "Epoch 10/10\n",
            "391/391 [==============================] - 10s 27ms/step - loss: 0.1810 - accuracy: 0.9366 - val_loss: 0.3132 - val_accuracy: 0.8797\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BoOmnxGj1D7w",
        "outputId": "d0d85929-72bb-4edb-ef9e-2ef671463119",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "def plot_graphs(history, string):\n",
        "  plt.plot(history.history[string])\n",
        "  plt.plot(history.history['val_'+string])\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(string)\n",
        "  plt.legend([string, 'val_'+string])\n",
        "  plt.show()\n",
        "  \n",
        "plot_graphs(history, \"accuracy\")\n",
        "plot_graphs(history, \"loss\")"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y73Wbj8y072d"
      },
      "source": [
        "e = model.layers[0]\n",
        "weights = e.get_weights()[0]\n",
        "print(weights.shape) # shape: (vocab_size, embedding_dim)\n",
        "\n",
        "import io\n",
        "\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, tokenizer.vocab_size):\n",
        "  word = tokenizer.decode([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()\n",
        "\n",
        "\n",
        "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": null,
      "outputs": []
    }
  ]
}