4. Short answer using Baseline.ipynb

{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import os\nimport json\nimport gc\nimport pickle\n\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm_notebook as tqdm\nimport tensorflow as tf\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.layers import *\nfrom tensorflow.keras.preprocessing import text, sequence\nfrom tensorflow.keras.utils import plot_model\nimport fasttext","execution_count":1,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# https://www.kaggle.com/opanichev/tf2-0-qa-binary-classification-baseline\ndef build_train(train_path, n_rows=200000):\n    with open(train_path) as f:\n        processed_rows = []\n\n        for i in tqdm(range(n_rows)):\n            line = f.readline()\n            if not line:\n                break\n\n            line = json.loads(line)\n\n            text = line['document_text'].split(' ')\n            question = line['question_text']\n            annotations = line['annotations'][0]\n            start_long = annotations['long_answer']['start_token']\n            if start_long == -1:\n                continue\n            end_long = annotations['long_answer']['end_token']\n             \n            if len(annotations['short_answers'])>0:\n                start_short = annotations['short_answers'][0]['start_token']\n                end_short = annotations['short_answers'][0]['end_token']\n                short = \" \".join(text[start_short:end_short])\n            else:\n                short = \"NONE\"\n            try:\n                processed_rows.append({\n                        'text': \" \".join(text[start_long:end_long]),\n                        'question': question,\n                        'short_answer':\"startseq \" + short + \" endseq\",\n                        'annotation_id': annotations['annotation_id']\n                })\n            except:\n                continue\n\n        train = pd.DataFrame(processed_rows)\n        \n        return train","execution_count":2,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def build_test(test_path, submission_path):\n    test = pd.read_json(test_path, orient='records', lines=True, dtype={'example_id':np.dtype('object')})\n    submission = pd.read_csv(submission_path)\n\n    short = '_short' \n    processed_rows = []\n    for i in tqdm(range(len(submission))):\n        if submission.iloc[i]['example_id'].endswith(short):\n            id = submission.iloc[i]['example_id'][:-6]\n            token = submission.iloc[i]['PredictionString']\n\n            if isinstance(token, str): # https://www.geeksforgeeks.org/python-check-if-a-variable-is-string/\n                # sample dataframe corresponding to the id\n                sample_df = test[test['example_id'] == id]\n                # Text of the sample df\n                text = sample_df.iloc[0]['document_text'].split()\n                # Corresponding Question \n                question = sample_df.iloc[0]['question_text'] \n                \n                # start: the token before \":\", end: the token after \":\"\n                index = token.index(':')\n                start = int(token[:index])\n                end = int(token[index+1:])\n                \n                # text corresponds to the long answer\n                text = \" \".join(text[start:end])\n                processed_rows.append({\n                        'text': text,\n                        'question': question, \n                        'annotation_id': id\n                })\n                \n            else:\n                # No long answer.\n                token = np.nan\n            \n    return pd.DataFrame(processed_rows)","execution_count":3,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_path = '/kaggle/input/tensorflow2-question-answering/simplified-nq-train.jsonl'\ntest_path = '/kaggle/input/tensorflow2-question-answering/simplified-nq-test.jsonl'\nsubmission_path = \"../input/baseline-lstm/submission.csv\"","execution_count":4,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train = build_train(train_path)\ntest= build_test(test_path, submission_path) ","execution_count":5,"outputs":[{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:6: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\nPlease use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n  \n","name":"stderr"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=200000.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c4d12c66fe0346859873b6fe89b7fd78"}},"metadata":{}},{"output_type":"stream","text":"\n","name":"stdout"},{"output_type":"stream","text":"/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:7: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\nPlease use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n  import sys\n","name":"stderr"},{"output_type":"display_data","data":{"text/plain":"HBox(children=(FloatProgress(value=0.0, max=692.0), HTML(value='')))","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e61e3b3bfd4942038d3924395797d821"}},"metadata":{}},{"output_type":"stream","text":"\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.head()","execution_count":6,"outputs":[{"output_type":"execute_result","execution_count":6,"data":{"text/plain":"                                                text  \\\n0  <P> A common example of permission marketing i...   \n1  <P> Tracy McConnell , better known as `` The M...   \n2  <P> The process of fertilization involves a sp...   \n3  <P> Active quarterback Tom Brady holds the rec...   \n4  <P> Pom Klementieff ( born 3 May 1986 ) is a F...   \n\n                                            question  \\\n0  which is the most common use of opt-in e-mail ...   \n1            how i.met your mother who is the mother   \n2   what type of fertilisation takes place in humans   \n3                   who had the most wins in the nfl   \n4        who played mantis guardians of the galaxy 2   \n\n                                        short_answer         annotation_id  \n0  startseq a newsletter sent to an advertising f...    593165450220027640  \n1                    startseq Tracy McConnell endseq  12034874153783787365  \n2                               startseq NONE endseq  10527123009892725162  \n3                          startseq Tom Brady endseq  14634796365152556576  \n4                    startseq Pom Klementieff endseq  12644762478285625867  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>text</th>\n      <th>question</th>\n      <th>short_answer</th>\n      <th>annotation_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>&lt;P&gt; A common example of permission marketing i...</td>\n      <td>which is the most common use of opt-in e-mail ...</td>\n      <td>startseq a newsletter sent to an advertising f...</td>\n      <td>593165450220027640</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>&lt;P&gt; Tracy McConnell , better known as `` The M...</td>\n      <td>how i.met your mother who is the mother</td>\n      <td>startseq Tracy McConnell endseq</td>\n      <td>12034874153783787365</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>&lt;P&gt; The process of fertilization involves a sp...</td>\n      <td>what type of fertilisation takes place in humans</td>\n      <td>startseq NONE endseq</td>\n      <td>10527123009892725162</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>&lt;P&gt; Active quarterback Tom Brady holds the rec...</td>\n      <td>who had the most wins in the nfl</td>\n      <td>startseq Tom Brady endseq</td>\n      <td>14634796365152556576</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>&lt;P&gt; Pom Klementieff ( born 3 May 1986 ) is a F...</td>\n      <td>who played mantis guardians of the galaxy 2</td>\n      <td>startseq Pom Klementieff endseq</td>\n      <td>12644762478285625867</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"test.head()","execution_count":7,"outputs":[{"output_type":"execute_result","execution_count":7,"data":{"text/plain":"                                                text  \\\n0  <P> Since 1997 , US courts have divided price ...   \n1  <P> The title You 've Come a Long Way , Baby w...   \n2  <P> Raghava and Nandini move into another hous...   \n3  <Tr> <Th> Location </Th> <Td> Tilak Marg , New...   \n4  <P> Brooke has an end of the summer party on t...   \n\n                                            question         annotation_id  \n0  association of producers that control supply a...  -1011141123527297803  \n1                 norman cook you've come a long way  -1028916936938579349  \n2  kanchana 2 film kanchana 2 film kanchana 2 fil...  -1055197305756217938  \n3  who appoint the judge of supreme court and hig...  -1074129516932871805  \n4     who released the time capsule in one tree hill  -1114334749483663139  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>text</th>\n      <th>question</th>\n      <th>annotation_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>&lt;P&gt; Since 1997 , US courts have divided price ...</td>\n      <td>association of producers that control supply a...</td>\n      <td>-1011141123527297803</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>&lt;P&gt; The title You 've Come a Long Way , Baby w...</td>\n      <td>norman cook you've come a long way</td>\n      <td>-1028916936938579349</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>&lt;P&gt; Raghava and Nandini move into another hous...</td>\n      <td>kanchana 2 film kanchana 2 film kanchana 2 fil...</td>\n      <td>-1055197305756217938</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>&lt;Tr&gt; &lt;Th&gt; Location &lt;/Th&gt; &lt;Td&gt; Tilak Marg , New...</td>\n      <td>who appoint the judge of supreme court and hig...</td>\n      <td>-1074129516932871805</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>&lt;P&gt; Brooke has an end of the summer party on t...</td>\n      <td>who released the time capsule in one tree hill</td>\n      <td>-1114334749483663139</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nX_train, X_test, Y_train, Y_test = train_test_split(train.drop(['short_answer'], 1), train['short_answer'])\ndel train","execution_count":8,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\ndef pad_sequence(t, padding_var = 20):\n    t = sequence.pad_sequences(t, maxlen=padding_var, padding='post') \n    return t\n\ndef build_train_X(X_train, X_test, test):\n    print(\"=\"*100)\n    print(\"Tokenizing the sequences...\")\n    \n    tokenizer = text.Tokenizer(lower=True, num_words=80000) \n    tokenizer.fit_on_texts(X_train['text'].append(X_train['question'], ignore_index=True).to_frame(name = 'text')['text'])\n    \n    print(\"Done\")\n    print(\"=\"*100)\n    print()\n    print(\"Starting Text to sequences process...\")\n    train_text = tokenizer.texts_to_sequences(X_train.text.values)\n    val_text = tokenizer.texts_to_sequences(X_train.question.values)\n    train_questions = tokenizer.texts_to_sequences(X_test.text.values)\n    val_questions = tokenizer.texts_to_sequences(X_test.question.values)\n    test_text = tokenizer.texts_to_sequences(test.text.values)\n    test_questions = tokenizer.texts_to_sequences(test.question.values)\n    \n    print(\"Done\")\n    print(\"=\"*100)\n    print()\n    \n    print(\"Padding Each Sequences...\")\n    train_text  = pad_sequence(train_text, 400)\n    val_text = pad_sequence(val_text, 400)\n    \n    train_questions = pad_sequence(train_questions, 20)\n    val_questions = pad_sequence(val_questions, 20)\n    \n    test_text = pad_sequence(test_text, 400)\n    test_questions = pad_sequence(test_questions, 20)  \n    print(\"Done\")\n    return train_text, val_text, train_questions, val_questions, test_text, test_questions, tokenizer\n\ndef build_y(Y_train, Y_test):\n    tokenizer = text.Tokenizer(lower=True)\n \n    tokenizer.fit_on_texts(Y_train)\n    print(\"Done\")\n    print(\"=\"*100)\n    print()\n    print(\"Starting Text to sequences process...\")\n    train_short_answer = tokenizer.texts_to_sequences(Y_train.values)\n    val_short_answer = tokenizer.texts_to_sequences(Y_test.values) \n    print(\"Done\")\n    print(\"=\"*100)\n    print()\n    \n    print(\"Padding Each Sequences...\")\n    \n    train_short_answer = pad_sequence(train_short_answer, 25)\n    val_short_answer = pad_sequence(val_short_answer, 25)\n     \n    print(\"Done\")\n    return train_short_answer, val_short_answer, tokenizer\n\n","execution_count":9,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_text, val_text, train_questions, val_questions, test_text, test_questions, tokenizer_X = build_train_X(X_train, X_test, test)","execution_count":10,"outputs":[{"output_type":"stream","text":"====================================================================================================\nTokenizing the sequences...\nDone\n====================================================================================================\n\nStarting Text to sequences process...\nDone\n====================================================================================================\n\nPadding Each Sequences...\nDone\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_short_answer, val_short_answer,tokenizer_Y = build_y(Y_train, Y_test)","execution_count":11,"outputs":[{"output_type":"stream","text":"Done\n====================================================================================================\n\nStarting Text to sequences process...\nDone\n====================================================================================================\n\nPadding Each Sequences...\nDone\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"del X_train, X_test, Y_train, Y_test","execution_count":12,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# saving\nwith open('tokenizer_X.pickle', 'wb') as handle:\n    pickle.dump(tokenizer_X, handle, protocol=pickle.HIGHEST_PROTOCOL)\n# saving\nwith open('tokenizer_Y.pickle', 'wb') as handle:\n    pickle.dump(tokenizer_Y, handle, protocol=pickle.HIGHEST_PROTOCOL)","execution_count":13,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import fasttext\ndef build_embedding_matrix_X(tokenizer, path):\n    embedding_matrix = np.zeros((tokenizer.num_words + 1, 300))\n    ft_model = fasttext.load_model(path)\n\n    for word, i in tokenizer.word_index.items():\n        if i >= tokenizer.num_words - 1:\n            break\n        embedding_matrix[i] = ft_model.get_word_vector(word)\n    \n    return embedding_matrix\npath = '/kaggle/input/fasttextsubword/crawl-300d-2M-subword.bin'\n\ndef build_embedding_matrix_Y(tokenizer, path):\n    embedding_matrix = np.zeros((len(tokenizer.word_counts) + 1, 300))\n    ft_model = fasttext.load_model(path)\n\n    for word, i in tokenizer.word_index.items():\n        if i >= len(tokenizer.word_counts) - 1:\n            break\n        embedding_matrix[i] = ft_model.get_word_vector(word)\n    \n    return embedding_matrix \n\nembedding_matrix_enc = build_embedding_matrix_X(tokenizer_X, path)\nembedding_matrix_dec = build_embedding_matrix_Y(tokenizer_Y, path)","execution_count":14,"outputs":[{"output_type":"stream","text":"Warning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\nWarning : `load_model` does not return WordVectorModel or SupervisedModel any more, but a `FastText` object which is very similar.\n","name":"stderr"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"embedding_matrix_enc.shape, embedding_matrix_dec.shape","execution_count":15,"outputs":[{"output_type":"execute_result","execution_count":15,"data":{"text/plain":"((80001, 300), (30400, 300))"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"vocab_size_enc = len(tokenizer_X.word_counts)\nvocab_size_dec = len(tokenizer_Y.word_counts)+1","execution_count":16,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def build_model(latent_dim=256):\n    \n    embedding_enc = Embedding(*embedding_matrix_enc.shape, weights=[embedding_matrix_enc], trainable=False, mask_zero=True)\n    embedding_dec = Embedding(*embedding_matrix_dec.shape, weights=[embedding_matrix_dec], trainable=False, mask_zero=True)\n    \n    # Encoder question\n    encoder_inputs_q = Input(shape=(None,), name=\"encoder_input_q\")\n    enc_emb_q = embedding_enc(encoder_inputs_q) \n    _, state_h_q, state_c_q = LSTM(latent_dim, return_state=True)(enc_emb_q)\n\n    # Encoder text\n    encoder_inputs_t = Input(shape=(None,), name=\"encoder_input_t\")\n    enc_emb_t = embedding_enc(encoder_inputs_t) \n    _, state_h_t, state_c_t = LSTM(latent_dim, return_state=True)(enc_emb_t)\n\n    encoder_states = [concatenate([state_h_q, state_h_t]), concatenate([state_c_q, state_c_t])]\n\n\n    # decoder Model\n    decoder_inputs = Input(shape=(None,), name=\"decoder_input\")\n    dec_emb_layer = embedding_dec(decoder_inputs)\n    decoder_outputs, _, _ = LSTM(2*latent_dim, return_sequences=True, return_state=True)(dec_emb_layer,initial_state=encoder_states)\n    decoder_outputs = Dense(vocab_size_dec, activation='softmax')(decoder_outputs)\n    \n    model = Model([encoder_inputs_q, encoder_inputs_t, decoder_inputs], decoder_outputs)\n    \n    # Encode the input sequence to get the \"thought vectors\"\n    encoder_model = Model([encoder_inputs_q, encoder_inputs_t], encoder_states)\n\n    # Decoder setup\n    # Below tensors will hold the states of the previous time step\n    decoder_state_input_h = Input(shape=(2*latent_dim,))\n    decoder_state_input_c = Input(shape=(2*latent_dim,))\n    decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]\n\n    dec_emb2= embedding_dec(decoder_inputs) # Get the embeddings of the decoder sequence\n\n    # To predict the next word in the sequence, set the initial states to the states from the previous time step\n    decoder_outputs2, state_h2, state_c2 = LSTM(2*latent_dim, return_sequences=True, return_state=True)(dec_emb2, initial_state=decoder_states_inputs)\n    decoder_states2 = [state_h2, state_c2]\n    decoder_outputs2 = Dense(vocab_size_dec, activation='softmax')(decoder_outputs2) # A dense softmax layer to generate prob dist. over the target vocabulary\n\n    # Final decoder model\n    decoder_model = Model([decoder_inputs] + decoder_states_inputs, [decoder_outputs2] + decoder_states2)    \n\n    return model, encoder_model, decoder_model","execution_count":17,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model, encoder_model, decoder_model = build_model()","execution_count":18,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plot_model(\n    model, to_file='model.png', show_shapes=True, show_layer_names=True,\n    rankdir='TB', expand_nested=False, dpi=96\n)","execution_count":19,"outputs":[{"output_type":"execute_result","execution_count":19,"data":{"image/png":"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\n","text/plain":"<IPython.core.display.Image 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dpi=96\n)","execution_count":20,"outputs":[{"output_type":"execute_result","execution_count":20,"data":{"image/png":"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\n","text/plain":"<IPython.core.display.Image object>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"plot_model(\n    decoder_model, to_file='decoder_model.png', show_shapes=True, show_layer_names=True,\n    rankdir='TB', expand_nested=False, dpi=96\n)","execution_count":21,"outputs":[{"output_type":"execute_result","execution_count":21,"data":{"image/png":"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\n","text/plain":"<IPython.core.display.Image object>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"def generate_batch(train_question, train_text, train_short_answer, batch_size = 128): \n    while True:\n        for j in range(0, len(train_question), batch_size):\n            encoder_input_q = np.array(train_question[j:j+batch_size])\n            encoder_input_t = np.array(train_text[j:j+batch_size])\n            decoder_input_data = np.array(train_short_answer[j:j+batch_size])\n            \n            decoder_target_data = np.zeros((encoder_input_q.shape[0], 25, vocab_size_dec),dtype='float32')\n            for i, target_text in enumerate(train_short_answer[j:j+batch_size]):\n                for t, index in enumerate(target_text):\n                    if t>0:\n                        # decoder target sequence (one hot encoded)\n                        # does not include the START_ token\n                        decoder_target_data[i, t - 1, index] = 1.\n           \n            yield([encoder_input_q, encoder_input_t, decoder_input_data], decoder_target_data)","execution_count":22,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])","execution_count":23,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_samples = len(train_questions)\nval_samples = len(val_questions)\nbatch_size = 128\nepochs = 50","execution_count":24,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.fit_generator(generator = generate_batch(train_questions, train_text, train_short_answer, batch_size = batch_size), \n                    steps_per_epoch = train_samples//batch_size,\n                    epochs=epochs,\n                    validation_data = generate_batch(val_questions, val_text, val_short_answer, batch_size = batch_size),\n                    validation_steps = val_samples//batch_size)","execution_count":25,"outputs":[{"output_type":"stream","text":"Epoch 1/50\n193/193 [==============================] - 240s 1s/step - loss: 1.1359 - acc: 0.1492 - val_loss: 0.9225 - val_acc: 0.0701\nEpoch 2/50\n193/193 [==============================] - 238s 1s/step - loss: 0.9150 - acc: 0.0916 - val_loss: 0.8748 - val_acc: 0.0867\nEpoch 3/50\n193/193 [==============================] - 237s 1s/step - loss: 0.8710 - acc: 0.0941 - val_loss: 0.8715 - val_acc: 0.0885\nEpoch 4/50\n193/193 [==============================] - 237s 1s/step - loss: 0.8441 - acc: 0.0947 - val_loss: 0.8698 - val_acc: 0.0896\nEpoch 5/50\n193/193 [==============================] - 237s 1s/step - loss: 0.8205 - acc: 0.0951 - val_loss: 0.8635 - val_acc: 0.0908\nEpoch 6/50\n193/193 [==============================] - 238s 1s/step - loss: 0.7973 - acc: 0.0953 - val_loss: 0.8642 - val_acc: 0.0913\nEpoch 7/50\n193/193 [==============================] - 238s 1s/step - loss: 0.7753 - acc: 0.0955 - val_loss: 0.8632 - val_acc: 0.0916\nEpoch 8/50\n193/193 [==============================] - 238s 1s/step - loss: 0.7554 - acc: 0.0958 - val_loss: 0.8600 - val_acc: 0.0917\nEpoch 9/50\n193/193 [==============================] - 238s 1s/step - loss: 0.7515 - acc: 0.0960 - val_loss: 0.8659 - val_acc: 0.0920\nEpoch 10/50\n193/193 [==============================] - 239s 1s/step - loss: 0.7436 - acc: 0.0962 - val_loss: 0.8705 - val_acc: 0.0916\nEpoch 11/50\n193/193 [==============================] - 238s 1s/step - loss: 0.7242 - acc: 0.0965 - val_loss: 0.8597 - val_acc: 0.0925\nEpoch 12/50\n193/193 [==============================] - 238s 1s/step - loss: 0.7076 - acc: 0.0967 - val_loss: 0.8600 - val_acc: 0.0933\nEpoch 13/50\n193/193 [==============================] - 238s 1s/step - loss: 0.6893 - acc: 0.0970 - val_loss: 0.8657 - val_acc: 0.0933\nEpoch 14/50\n193/193 [==============================] - 239s 1s/step - loss: 0.6726 - acc: 0.0974 - val_loss: 0.8751 - val_acc: 0.0930\nEpoch 15/50\n193/193 [==============================] - 238s 1s/step - loss: 0.6606 - acc: 0.0978 - val_loss: 0.8830 - val_acc: 0.0932\nEpoch 16/50\n193/193 [==============================] - 238s 1s/step - loss: 0.6522 - acc: 0.0983 - val_loss: 0.8771 - val_acc: 0.0933\nEpoch 17/50\n193/193 [==============================] - 239s 1s/step - loss: 0.6370 - acc: 0.0990 - val_loss: 0.8887 - val_acc: 0.0930\nEpoch 18/50\n193/193 [==============================] - 238s 1s/step - loss: 0.6379 - acc: 0.0995 - val_loss: 0.8760 - val_acc: 0.0936\nEpoch 19/50\n193/193 [==============================] - 239s 1s/step - loss: 0.6207 - acc: 0.1005 - val_loss: 0.8821 - val_acc: 0.0935\nEpoch 20/50\n193/193 [==============================] - 239s 1s/step - loss: 0.6115 - acc: 0.1012 - val_loss: 0.8949 - val_acc: 0.0933\nEpoch 21/50\n193/193 [==============================] - 239s 1s/step - loss: 0.5974 - acc: 0.1024 - val_loss: 0.8938 - val_acc: 0.0933\nEpoch 22/50\n193/193 [==============================] - 239s 1s/step - loss: 0.5827 - acc: 0.1036 - val_loss: 0.9031 - val_acc: 0.0933\nEpoch 23/50\n193/193 [==============================] - 238s 1s/step - loss: 0.5926 - acc: 0.1041 - val_loss: 0.8918 - val_acc: 0.0936\nEpoch 24/50\n193/193 [==============================] - 238s 1s/step - loss: 0.5807 - acc: 0.1052 - val_loss: 0.8904 - val_acc: 0.0937\nEpoch 25/50\n193/193 [==============================] - 239s 1s/step - loss: 0.5666 - acc: 0.1065 - val_loss: 0.8926 - val_acc: 0.0938\nEpoch 26/50\n193/193 [==============================] - 238s 1s/step - loss: 0.5584 - acc: 0.1077 - val_loss: 0.8976 - val_acc: 0.0938\nEpoch 27/50\n193/193 [==============================] - 239s 1s/step - loss: 0.5519 - acc: 0.1085 - val_loss: 0.8984 - val_acc: 0.0938\nEpoch 28/50\n193/193 [==============================] - 238s 1s/step - loss: 0.5405 - acc: 0.1097 - val_loss: 0.9071 - val_acc: 0.0937\nEpoch 29/50\n193/193 [==============================] - 239s 1s/step - loss: 0.5296 - acc: 0.1108 - val_loss: 0.9016 - val_acc: 0.0938\nEpoch 30/50\n193/193 [==============================] - 239s 1s/step - loss: 0.5216 - acc: 0.1117 - val_loss: 0.9108 - val_acc: 0.0936\nEpoch 31/50\n193/193 [==============================] - 239s 1s/step - loss: 0.5086 - acc: 0.1131 - val_loss: 0.9138 - val_acc: 0.0936\nEpoch 32/50\n193/193 [==============================] - 238s 1s/step - loss: 0.4972 - acc: 0.1144 - val_loss: 0.9184 - val_acc: 0.0934\nEpoch 33/50\n193/193 [==============================] - 239s 1s/step - loss: 0.4897 - acc: 0.1155 - val_loss: 0.9255 - val_acc: 0.0934\nEpoch 34/50\n193/193 [==============================] - 239s 1s/step - loss: 0.4796 - acc: 0.1166 - val_loss: 0.9312 - val_acc: 0.0933\nEpoch 35/50\n193/193 [==============================] - 239s 1s/step - loss: 0.4718 - acc: 0.1176 - val_loss: 0.9283 - val_acc: 0.0935\nEpoch 36/50\n193/193 [==============================] - 238s 1s/step - loss: 0.4635 - acc: 0.1186 - val_loss: 0.9368 - val_acc: 0.0933\nEpoch 37/50\n193/193 [==============================] - 239s 1s/step - loss: 0.4567 - acc: 0.1196 - val_loss: 0.9429 - val_acc: 0.0931\nEpoch 38/50\n193/193 [==============================] - 239s 1s/step - loss: 0.4505 - acc: 0.1205 - val_loss: 0.9458 - val_acc: 0.0930\nEpoch 39/50\n193/193 [==============================] - 240s 1s/step - loss: 0.4405 - acc: 0.1220 - val_loss: 0.9567 - val_acc: 0.0930\nEpoch 40/50\n193/193 [==============================] - 239s 1s/step - loss: 0.4340 - acc: 0.1230 - val_loss: 0.9648 - val_acc: 0.0925\nEpoch 41/50\n193/193 [==============================] - 239s 1s/step - loss: 0.4261 - acc: 0.1241 - val_loss: 0.9639 - val_acc: 0.0926\nEpoch 42/50\n193/193 [==============================] - 240s 1s/step - loss: 0.4187 - acc: 0.1251 - val_loss: 0.9764 - val_acc: 0.0921\nEpoch 43/50\n193/193 [==============================] - 239s 1s/step - loss: 0.4114 - acc: 0.1263 - val_loss: 0.9740 - val_acc: 0.0924\nEpoch 44/50\n193/193 [==============================] - 239s 1s/step - loss: 0.4039 - acc: 0.1273 - val_loss: 0.9729 - val_acc: 0.0925\nEpoch 45/50\n193/193 [==============================] - 239s 1s/step - loss: 0.3982 - acc: 0.1283 - val_loss: 0.9790 - val_acc: 0.0922\nEpoch 46/50\n193/193 [==============================] - 238s 1s/step - loss: 0.3916 - acc: 0.1294 - val_loss: 0.9830 - val_acc: 0.0921\nEpoch 47/50\n193/193 [==============================] - 239s 1s/step - loss: 0.3845 - acc: 0.1306 - val_loss: 0.9902 - val_acc: 0.0918\nEpoch 48/50\n193/193 [==============================] - 240s 1s/step - loss: 0.3767 - acc: 0.1315 - val_loss: 0.9903 - val_acc: 0.0921\nEpoch 49/50\n193/193 [==============================] - 239s 1s/step - loss: 0.3776 - acc: 0.1313 - val_loss: 0.9899 - val_acc: 0.0923\nEpoch 50/50\n193/193 [==============================] - 239s 1s/step - loss: 0.3681 - acc: 0.1330 - val_loss: 0.9998 - val_acc: 0.0921\n","name":"stdout"},{"output_type":"execute_result","execution_count":25,"data":{"text/plain":"<tensorflow.python.keras.callbacks.History at 0x7fd85164b850>"},"metadata":{}}]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.save_weights('model_baseline_short.h5')","execution_count":26,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.load_weights('model_baseline_short.h5')","execution_count":27,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def decode_sequence(text, question):\n    # Encode the input as state vectors.\n    input_seq = [np.array(question).reshape(1, 20),np.array(text.reshape(1, 400)) ]\n    states_value = encoder_model.predict(input_seq)\n    # Generate empty target sequence of length 1.\n    target_seq = np.zeros((1,1))\n    # Populate the first character of target sequence with the start character.\n    target_seq[0, 0] = tokenizer_Y.word_index['startseq']\n \n    stop_condition = False\n    decoded_sentence = ''\n    while not stop_condition:\n        output_tokens, h, c = decoder_model.predict([target_seq] + states_value)\n\n        # Sample a token\n        sampled_token_index = np.argmax(output_tokens[0, -1, :])\n        sampled_char = tokenizer_Y.index_word[sampled_token_index]\n        decoded_sentence += ' '+sampled_char \n        # Exit condition: either hit max length\n        # or find stop character.\n        if (sampled_char == 'endseq' or\n           len(decoded_sentence.split()) > 25):\n            stop_condition = True\n\n        # Update the target sequence (of length 1).\n        target_seq = np.zeros((1,1))\n        target_seq[0, 0] = sampled_token_index\n\n        # Update states\n        states_value = [h, c]\n\n    return decoded_sentence","execution_count":37,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"annotation_id: {}\".format(test.iloc[0]['annotation_id']))\nprint(\"Text: {}...\".format(test.iloc[0]['text'][:200]))\nprint(\"Question: {}\".format(test.iloc[0]['question']))\nprint()\nprint(\"Predicted Answer: {}\".format( decode_sequence(test_text[0], test_questions[0]) ))","execution_count":59,"outputs":[{"output_type":"stream","text":"annotation_id: -1011141123527297803\nText: <P> Since 1997 , US courts have divided price fixing into two categories : vertical and horizontal maximum price fixing . Vertical price fixing includes a manufacturer 's attempt to control the price ...\nQuestion: association of producers that control supply and prices\n\nPredicted Answer: Since 1997 , US courts have divided price fixing\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"annotation_id: {}\".format(test.iloc[1]['annotation_id']))\nprint(\"Text: {}...\".format(test.iloc[1]['text'][:200]))\nprint(\"Question: {}\".format(test.iloc[1]['question']))\nprint()\nprint(\"Predicted Answer: {}\".format( decode_sequence(test_text[1], test_questions[1]) ))","execution_count":68,"outputs":[{"output_type":"stream","text":"annotation_id: -1028916936938579349\nText: <P> The title You 've Come a Long Way , Baby was derived from a marketing slogan for Virginia Slims cigarettes . Conceived by Red Design , the album 's primary cover art features an obese young man dr...\nQuestion: norman cook you've come a long way\n\nPredicted Answer: slogan for Virginia Slims cigarettes . Conceived by Red Design , the album 's primary cover art features an obese young\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"annotation_id: {}\".format(test.iloc[3]['annotation_id']))\nprint(\"Text: {}...\".format(test.iloc[3]['text'][:200]))\nprint(\"Question: {}\".format(test.iloc[3]['question']))\nprint()\nprint(\"Predicted Answer: {}\".format( decode_sequence(test_text[3], test_questions[3]) ))","execution_count":73,"outputs":[{"output_type":"stream","text":"annotation_id: -1074129516932871805\nText: <Tr> <Th> Location </Th> <Td> Tilak Marg , New Delhi , Delhi </Td> </Tr>...\nQuestion: who appoint the judge of supreme court and high court\n\nPredicted Answer: Tilak Marg\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"annotation_id: {}\".format(test.iloc[4]['annotation_id']))\nprint(\"Text: {}...\".format(test.iloc[4]['text'][:200]))\nprint(\"Question: {}\".format(test.iloc[4]['question']))\nprint()\nprint(\"Predicted Answer: {}\".format( decode_sequence(test_text[4], test_questions[4]) ))","execution_count":77,"outputs":[{"output_type":"stream","text":"annotation_id: -1114334749483663139\nText: <P> Brooke has an end of the summer party on the beach . Nathan is home from basketball camp . Lucas deals with Brooke 's rules for non-exclusive dating . Brooke ends up having a ball with many guys w...\nQuestion: who released the time capsule in one tree hill\n\nPredicted Answer: NONE\n","name":"stdout"}]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"annotation_id: {}\".format(test.iloc[5]['annotation_id']))\nprint(\"Text: {}...\".format(test.iloc[5]['text'][:200]))\nprint(\"Question: {}\".format(test.iloc[5]['question']))\nprint()\nprint(\"Predicted Answer: {}\".format( decode_sequence(test_text[5], test_questions[5]) ))","execution_count":86,"outputs":[{"output_type":"stream","text":"annotation_id: -1152268629614456016\nText: <P> Server Core does not include the . NET Framework , Internet Explorer , Windows PowerShell or many other features not related to core server features . A Server Core machine can be configured for s...\nQuestion: what feature introduced with windows server 2008 provides only a limited gui\n\nPredicted Answer: NET Framework , Internet Explorer , Windows PowerShell or many other features\n","name":"stdout"}]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}