neural_taskonoomy_narratives_ShallowSyntax.ipynb

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   "metadata": {
    "id": "SZHYJNM9VKP2"
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   "source": [
    "from transformers import BertTokenizer, BertModel, XLNetTokenizer, XLNetModel\n",
    "import torch\n",
    "from transformers import RobertaTokenizer, RobertaModel, AutoTokenizer, AutoModel\n",
    "import json\n",
    "import numpy as np"
   ]
  },
  {
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     "text": [
      "Some weights of the model checkpoint at vblagoje/bert-english-uncased-finetuned-chunk were not used when initializing BertModel: ['classifier.bias', 'classifier.weight']\n",
      "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "sent_bert_avg = []\n",
    "tokenizer = BertTokenizer.from_pretrained('vblagoje/bert-english-uncased-finetuned-chunk')\n",
    "model = BertModel.from_pretrained('vblagoje/bert-english-uncased-finetuned-chunk')"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BertModel(\n",
       "  (embeddings): BertEmbeddings(\n",
       "    (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
       "    (position_embeddings): Embedding(512, 768)\n",
       "    (token_type_embeddings): Embedding(2, 768)\n",
       "    (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "    (dropout): Dropout(p=0.1, inplace=False)\n",
       "  )\n",
       "  (encoder): BertEncoder(\n",
       "    (layer): ModuleList(\n",
       "      (0): BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (1): BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (2): BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (3): BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (4): BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (5): BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (6): BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (7): BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (8): BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (9): BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (10): BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "      (11): BertLayer(\n",
       "        (attention): BertAttention(\n",
       "          (self): BertSelfAttention(\n",
       "            (query): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (key): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (value): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "          (output): BertSelfOutput(\n",
       "            (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "        (intermediate): BertIntermediate(\n",
       "          (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
       "        )\n",
       "        (output): BertOutput(\n",
       "          (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
       "          (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
       "          (dropout): Dropout(p=0.1, inplace=False)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (pooler): BertPooler(\n",
       "    (dense): Linear(in_features=768, out_features=768, bias=True)\n",
       "    (activation): Tanh()\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "EilUhMS3EjoN"
   },
   "outputs": [],
   "source": [
    "transcript = 'I began my illustrious career in journalism in the Bronx where I toiled as a hard boiled reporter for the Ram the student newspaper at Fordham University. And one day I’m walking toward the campus center and out comes the elusive Dean McGowan architect of a policy to replace Fordham’s traditionally working to middle class students with wealthier more prestigious ones. So I whip out my notebook and I go up to him and I say Dean McGowan is it true that Fordham University plans to raise tuition substantially above the inflation rate and if so wouldn’t that be a betrayal of its mission And he stops and looks at me and he says Listen up punk. And right then there’s a blur in the corner of my eye which becomes this figure holding a cream pie which becomes the guy standing next to me mashing a cream pie into Dean McGowan’s face. And then runs away. And the Dean is covered with cream. So I give him a moment and then I say Dean McGowan would you care to comment. on this latest attack And the Dean says Yes I would care to comment Fuck you. So I race back to the newsroom with my scoop and I find the editor Jim Dwyer who’s a senior and he will go on to win a Pulitzer Prize that is true and he’s a big guy. I pitch him my story I tell him what I’ve seen and he says Dean McGowan that guy’s a dick. Write it up. So I’m banging out my story and I know it’s good and then I start to make it better by adding an element of embellishment. Reporters call this making shit up. And they recommend against crossing that line. But I had just seen the line crossed between a high powered dean and assault with a pastry and I kind of liked it. So the first I did was I gave the figure a name. I called him Pie Man capital P capital M. And I described him as a cape wearing masked avenger. Though in fact he’d been capeless. And said that as he fled the scene he clicked his heels in rakish glee. And I gave him a catchphrase in Latin. I said that he cried out Ego sum non an bestia which means I am not an animal. Which makes no sense. I needed something. I’m Catholic Latin just comes to me. So I finish my story and I hand it. to Dwyer and he reads and he says Pie Man I love it Page one. And that’s how the first line got crossed. A few days later I get a letter. It says Dear Jim good story. Nice details. If you want to see me again in action be on the steps of Duane Library Tuesday at 3 o’clock signed Pie Man. So I was there with a photographer from the Ram. And sure enough out comes Sheila Beale student body president. And now Sheila was different from me and all the other Fordham students who wore flannel shirts and worked part time jobs. Sheila was well bred. Sheila had school spirit. Sheila was the kind of. student that Dean McGown wanted more of Although rumor had it that he got plenty of her in his office on his desk. But that’s just a rumor. please do not spread it outside this rumor I myself would never say that. But the fact was Sheila had collaborated with the Dean to ban outdoor drinking on campus. The infamous no more beer at barbecues rule. That’s right boo that rule. Sheila thought drinking in public was in poor taste. I think you know what happened next. Pie Man emerged from behind the late night library drop box made his delivery and fled away crying Ego sum non an bestia. Or at least that’s what it said in my story in the newspaper the next day which ran with a photo of him leaving the scene cape flowing behind him doing this. And that’s what made him a sensation on campus. People started dressing like him and quoting him in class. The Ram ran five major stories about Pie Man all of them by me. And toward the end of this run I was out at a bar one night and I saw I came and I saw in the corner Angela from my Brit Lit class drinking with some friends. And now Angela and I had been flirting for two months. Or I had been flirting with her and with such nuance that there was a question about whether she knew I existed. So I I saw her there and made a mental note to do nothing about it. And then I went to the bar to buy a round and I felt a tap on my shoulder. And it was her. And she said Jim we were just talking about how you always seem to know when and where Pie Man will strike. And we were wondering are you Pie Man And I knew by the way she said it I knew that if I said yes she would have sex with me. And wasn’t I really Pie Man For having brought him into being. Didn’t she only know about him through me But she had asked me a straightforward question that came with a straightforward answer. In fact I wasn’t Pie Man. As far as I knew I had never seen the guy out of costume. So I looked at her and I said Yes Angela. I am Pie Man. And she said Oh good. Now buy me a beer and tell me all about it.'\n",
    "sentences = transcript.strip().split('.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-_E1NBZOE3yh",
    "outputId": "04d71484-89c9-44f6-f527-454bf86a56bf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], [8, 9, 10, 11, 12, 13, 14, 15, 16, 17], [16, 17, 18, 19, 20, 21, 22, 23, 24, 25], [24, 25, 26, 27, 28, 29, 30, 31, 32, 33], [32, 33, 34, 35, 36, 37, 38, 39, 40, 41], [40, 41, 42, 43, 44, 45, 46, 47, 48, 49], [48, 49, 50, 51, 52, 53, 54, 55, 56, 57], [56, 57, 58, 59, 60, 61, 62, 63, 64, 65], [64, 65, 66]]\n"
     ]
    }
   ],
   "source": [
    "A = [i for i in range(0,len(sentences)-1)]\n",
    "size = 10\n",
    "step = 8\n",
    "A = [A[i : i + size] for i in range(0, len(A), step)]\n",
    "print(A)\n",
    "grouped_sentences = []\n",
    "for group in A:\n",
    "  g = \"\"\n",
    "  for i in group:\n",
    "    g += sentences[i].strip() + '. '\n",
    "  grouped_sentences.append(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "OaeqE62MjxVU",
    "outputId": "17b3dada-8fd6-42f2-d80c-4f667b36fb9d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'I am Pie Man. And she said Oh good. Now buy me a beer and tell me all about it. '"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_sentences[8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1EIRGYvZgvK-",
    "outputId": "621b5b8d-a5b8-44aa-ebde-33ae6e34f3c1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(299, 768)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-d5efa147e9cf>:20: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  grouped_features.append(np.array(group))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(184, 768)\n",
      "(112, 768)\n",
      "(116, 768)\n",
      "(131, 768)\n",
      "(153, 768)\n",
      "(178, 768)\n",
      "(147, 768)\n",
      "(25, 768)\n"
     ]
    }
   ],
   "source": [
    "grouped_features = []\n",
    "\n",
    "for text in grouped_sentences:\n",
    "  group = []\n",
    "  enc = tokenizer.encode(text)\n",
    "  input_ids = torch.tensor(enc).unsqueeze(0)\n",
    "  outputs = model(input_ids)\n",
    "  enc = np.array(enc)\n",
    "  ree = np.where(enc == 1012)\n",
    "  ree = ree[0]\n",
    "  outputs = outputs[0][0].detach().numpy()\n",
    "  print(outputs.shape)\n",
    "  group.append(outputs[1:ree[0]+1])\n",
    "  for i in range(0,len(ree)):\n",
    "    if(i != len(ree)-1):\n",
    "      group.append(outputs[ree[i]+1:ree[i+1]+1])\n",
    "    else:\n",
    "      break\n",
    "      # group.append(outputs[ree[i]+1:len(outputs)-1])\n",
    "  grouped_features.append(np.array(group))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[101, 1012, 102]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.encode('.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[ S E P ]'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.decode(102)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "SS8ZDDlbocXB"
   },
   "outputs": [],
   "source": [
    "sentences_features = []\n",
    "for i in range(0,len(grouped_features)):\n",
    "  for j in range(0,len(grouped_features[i])):\n",
    "    # print(i,j)\n",
    "    if(i == 0):\n",
    "      if(j < len(grouped_features[i])-2):\n",
    "        sentences_features.append(grouped_features[i][j])\n",
    "      elif(j == len(grouped_features[i])-2):\n",
    "        sentences_features.append((grouped_features[i][j]+grouped_features[i+1][0])/2)\n",
    "      elif(j == len(grouped_features[i])-1):\n",
    "        sentences_features.append((grouped_features[i][j]+grouped_features[i+1][1])/2)\n",
    "    elif(i != 0 and i != len(grouped_features)-1):\n",
    "      if(j < 2):\n",
    "        continue\n",
    "      elif(j < len(grouped_features[i])-2):\n",
    "        sentences_features.append(grouped_features[i][j])\n",
    "      elif(j == len(grouped_features[i])-2):\n",
    "        sentences_features.append((grouped_features[i][j]+grouped_features[i+1][0])/2)\n",
    "      elif(j == len(grouped_features[i])-1):\n",
    "        sentences_features.append((grouped_features[i][j]+grouped_features[i+1][1])/2)\n",
    "    elif(i == len(grouped_features)-1):\n",
    "      if(j < 2):\n",
    "        continue\n",
    "      else:\n",
    "        sentences_features.append(grouped_features[i][j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mHlqIMN6sL6Q",
    "outputId": "7d83b25f-46ea-4184-acb2-88805ec26660"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-12-9bc91c5bf0b5>:1: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  sentences_features = np.array(sentences_features)\n"
     ]
    }
   ],
   "source": [
    "sentences_features = np.array(sentences_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "dog0SnVRwEi0"
   },
   "outputs": [],
   "source": [
    "tr_data = ['',\n",
    " '',\n",
    " '',\n",
    " '',\n",
    " '',\n",
    " '',\n",
    " '',\n",
    " '',\n",
    " '',\n",
    " '',\n",
    " 'I began my illustrious career',\n",
    " 'career in journalism',\n",
    " 'in the Bronx where I toiled',\n",
    " 'toiled as a hard boiled',\n",
    " 'boiled reporter for the Ram',\n",
    " 'Ram the',\n",
    " 'the student newspaper at Fordham',\n",
    " 'Fordham University',\n",
    " 'And one day I’m walking toward the campus center',\n",
    " 'center and out comes the elusive',\n",
    " 'elusive Dean McGowan',\n",
    " 'McGowan architect of a',\n",
    " 'policy to replace',\n",
    " 'replace Fordham’s traditionally',\n",
    " 'traditionally working to middle class',\n",
    " 'class students with',\n",
    " 'with wealthier more prestigious',\n",
    " 'prestigious ones',\n",
    " 'So I whip out my notebook and I go up',\n",
    " 'up to him and I say Dean McGowan',\n",
    " 'is it true that Fordham',\n",
    " 'Fordham University plans to raise tuition',\n",
    " 'tuition substantially above the inflation',\n",
    " 'inflation rate and if so',\n",
    " 'wouldn’t that be a betrayal of its mission',\n",
    " 'mission',\n",
    " 'And he stops',\n",
    " 'stops and looks at me',\n",
    " 'and he says',\n",
    " 'Listen up punk And right',\n",
    " 'right then there’s a blur',\n",
    " 'blur in the corner of my eye which becomes',\n",
    " 'becomes this figure',\n",
    " 'holding a cream pie which becomes',\n",
    " 'becomes the guy standing',\n",
    " 'standing next to me',\n",
    " 'mashing a cream pie',\n",
    " 'pie into Dean McGowan’s face',\n",
    " 'face',\n",
    " 'And then runs away',\n",
    " '',\n",
    " 'And the Dean',\n",
    " 'Dean is covered',\n",
    " 'covered with cream',\n",
    " 'So I',\n",
    " 'I give him a moment',\n",
    " '',\n",
    " 'and then I say Dean McGowan would you care',\n",
    " 'care to comment on this latest attack',\n",
    " 'attack And',\n",
    " '',\n",
    " 'the Dean says Yes',\n",
    " 'Yes I would care to comment',\n",
    " 'comment Fuck you',\n",
    " 'you',\n",
    " 'So I race back to',\n",
    " 'to the newsroom with my scoop',\n",
    " 'and I find the editor',\n",
    " 'editor Jim Dwyer who’s a senior',\n",
    " 'senior and he will go',\n",
    " 'go on to win a Pulitzer Prize that is',\n",
    " 'is true',\n",
    " 'and he’s a big guy I pitch',\n",
    " 'pitch him my story I tell him what I’ve seen',\n",
    " 'seen and he says',\n",
    " 'Dean McGowan',\n",
    " 'McGowan that guy’s a',\n",
    " 'a dick Write it up',\n",
    " 'So I’m banging',\n",
    " 'banging out my story and I know it’s good',\n",
    " 'good and then I start to',\n",
    " 'to make it better',\n",
    " 'better',\n",
    " '',\n",
    " 'by adding an element of',\n",
    " 'of embellishment',\n",
    " 'Reporters call this',\n",
    " 'this making shit',\n",
    " 'shit up',\n",
    " '',\n",
    " 'And they recommend',\n",
    " 'recommend against crossing that line',\n",
    " 'line',\n",
    " 'But I had just seen',\n",
    " 'seen the line crossed',\n",
    " 'crossed between a high powered dean',\n",
    " 'dean and assault with a pastry',\n",
    " 'pastry and I',\n",
    " 'I kind of liked it',\n",
    " 'So the first I did',\n",
    " 'was I gave the figure a name',\n",
    " 'name I called him Pie Man',\n",
    " 'Man capital P capital M',\n",
    " 'M And I described',\n",
    " 'described him as a cape wearing masked',\n",
    " 'masked avenger',\n",
    " 'Though in fact he’d been',\n",
    " 'been capeless',\n",
    " 'And said that as he fled the scene',\n",
    " 'scene he clicked his heels',\n",
    " 'heels in rakish glee',\n",
    " '',\n",
    " 'And I gave',\n",
    " 'gave him a catchphrase',\n",
    " 'in Latin',\n",
    " '',\n",
    " '',\n",
    " 'I said that he cried out',\n",
    " 'Ego sum',\n",
    " 'non an bestia',\n",
    " 'bestia',\n",
    " 'which means I am',\n",
    " 'am not an animal',\n",
    " 'animal',\n",
    " 'Which makes no sense',\n",
    " 'sense',\n",
    " 'I needed something',\n",
    " 'something I’m Catholic',\n",
    " 'Catholic Latin just comes to me',\n",
    " 'me',\n",
    " 'So I finish my story',\n",
    " 'and I hand it to Dwyer and he reads',\n",
    " 'reads and he says Pie',\n",
    " 'Pie Man',\n",
    " 'I love it Page one',\n",
    " '',\n",
    " 'And that’s how',\n",
    " 'how the first line got',\n",
    " 'got crossed',\n",
    " 'A few days later',\n",
    " 'later I get a letter',\n",
    " 'It says Dear Jim',\n",
    " 'Jim good story Nice',\n",
    " 'Nice details',\n",
    " 'If you want to see me again',\n",
    " 'again in action',\n",
    " 'be on the steps of Duane',\n",
    " 'Duane Library Tuesday',\n",
    " 'at 3 o’clock',\n",
    " 'signed',\n",
    " 'Pie Man',\n",
    " '',\n",
    " 'So I was',\n",
    " 'was there with',\n",
    " 'with a photographer from the Ram',\n",
    " 'And sure enough',\n",
    " 'out comes Sheila Beale',\n",
    " 'student body president',\n",
    " 'president And now Sheila',\n",
    " 'Sheila was different from me',\n",
    " 'and all the other Fordham students',\n",
    " 'students who wore flannel shirts and',\n",
    " 'and worked part time jobs',\n",
    " 'Sheila was',\n",
    " 'well bred Sheila',\n",
    " 'Sheila had school',\n",
    " 'school spirit',\n",
    " 'Sheila',\n",
    " 'Sheila was the kind of student that Dean McGown',\n",
    " 'McGown wanted more of',\n",
    " 'Although rumor',\n",
    " 'rumor had it that he got plenty of',\n",
    " 'of her in his office on',\n",
    " 'on his desk',\n",
    " 'But that’s',\n",
    " 'that’s just a rumor please do not spread it',\n",
    " 'it outside this rumor',\n",
    " 'I myself would never',\n",
    " 'never say that',\n",
    " 'But the fact was Sheila had',\n",
    " 'had collaborated with the Dean',\n",
    " 'Dean to ban outdoor',\n",
    " 'outdoor drinking on campus',\n",
    " 'The infamous no more',\n",
    " 'more beer at barbecues rule That’s',\n",
    " 'That’s right boo that rule',\n",
    " '',\n",
    " 'Sheila thought drinking',\n",
    " 'drinking in public was in',\n",
    " 'in poor taste',\n",
    " 'I',\n",
    " 'I think you know what happened',\n",
    " 'happened next',\n",
    " 'Pie Man emerged',\n",
    " 'emerged from behind the',\n",
    " 'the late night library drop box',\n",
    " 'box made his delivery',\n",
    " 'delivery and fled away',\n",
    " 'crying',\n",
    " 'Ego sum non',\n",
    " 'non an bestia',\n",
    " '',\n",
    " 'Or at least',\n",
    " 'least that’s what it said in my story in the',\n",
    " 'the newspaper the next day',\n",
    " 'which ran with a',\n",
    " 'a photo of him',\n",
    " 'leaving the scene cape',\n",
    " 'cape flowing behind him',\n",
    " 'him doing this',\n",
    " '',\n",
    " 'And that’s',\n",
    " 'that’s what made him a sensation',\n",
    " 'sensation on campus',\n",
    " 'People started dressing like him',\n",
    " 'him and quoting him in class',\n",
    " 'class The Ram ran',\n",
    " 'ran five major stories about Pie',\n",
    " 'Pie Man all of them by me',\n",
    " 'me And',\n",
    " 'And toward the end of this run I was out at a bar',\n",
    " 'bar one night and I saw',\n",
    " 'I came and I saw in the',\n",
    " 'the corner',\n",
    " 'Angela from my Brit',\n",
    " 'Brit Lit class drinking',\n",
    " 'drinking with some friends And now Angela',\n",
    " 'Angela and I had been flirting',\n",
    " 'flirting for two months',\n",
    " 'Or I',\n",
    " 'I had been flirting with her',\n",
    " 'and with',\n",
    " 'with such nuance that',\n",
    " 'that there was a question about whether she',\n",
    " 'she knew I existed',\n",
    " '',\n",
    " 'So I I saw her there',\n",
    " 'there and made a mental note to do nothing about',\n",
    " 'about it',\n",
    " 'And then I went to the bar',\n",
    " 'bar to buy',\n",
    " 'buy a round and I felt',\n",
    " 'felt a tap on my shoulder',\n",
    " 'And it was her',\n",
    " 'her And',\n",
    " 'And she said',\n",
    " 'Jim we were just talking about',\n",
    " 'about how you always seem',\n",
    " 'seem to know when',\n",
    " 'when and where Pie',\n",
    " 'Pie Man will strike',\n",
    " 'And we were wondering',\n",
    " 'wondering are',\n",
    " 'are you Pie Man',\n",
    " 'Man And',\n",
    " 'I',\n",
    " 'I knew by the way she said it I',\n",
    " 'I knew that',\n",
    " 'that if I said yes she would have sex',\n",
    " 'sex with me',\n",
    " '',\n",
    " 'And wasn’t',\n",
    " 'wasn’t I really Pie Man',\n",
    " 'Man',\n",
    " '',\n",
    " 'For having',\n",
    " 'having brought him into being',\n",
    " 'being Didn’t she only know',\n",
    " 'know about him through me',\n",
    " '',\n",
    " 'But she had asked me a straightforward',\n",
    " 'straightforward question that came with',\n",
    " 'with a straightforward answer In fact',\n",
    " 'fact I wasn’t Pie',\n",
    " 'Pie Man As far as I knew I had never',\n",
    " 'never seen the guy',\n",
    " 'guy out of costume',\n",
    " 'So I looked at her and I',\n",
    " 'I said',\n",
    " 'Yes',\n",
    " 'Yes Angela',\n",
    " 'I am Pie Man',\n",
    " '',\n",
    " 'And she said Oh good',\n",
    " 'Now buy me a beer',\n",
    " 'beer and tell me all',\n",
    " 'all about it']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "V5uv2-692YZg",
    "outputId": "242bb081-7d4f-4b72-b8e9-b0326a79d6bb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[101, 119, 102]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.encode('.')\n",
    "#tokenizer.decode(103)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SlJ6SLTA3ipH",
    "outputId": "d4dcee55-264f-4f79-c668-138505a4408b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I began my illustrious career\n",
      "0 0\n",
      "1 0\n",
      "2 0\n",
      "3 0\n",
      "4 0\n",
      "(5, 768)\n",
      "career in journalism\n",
      "0 0\n",
      "1 0\n",
      "2 0\n",
      "(3, 768)\n",
      "in the Bronx where I toiled\n",
      "0 0\n",
      "1 0\n",
      "2 0\n",
      "3 0\n",
      "4 0\n",
      "5 0\n",
      "6 0\n",
      "(7, 768)\n",
      "toiled as a hard boiled\n",
      "0 0\n",
      "1 0\n",
      "2 0\n",
      "3 0\n",
      "4 0\n",
      "5 0\n",
      "(6, 768)\n",
      "boiled reporter for the Ram\n",
      "0 0\n",
      "1 0\n",
      "2 0\n",
      "3 0\n",
      "4 0\n",
      "(5, 768)\n",
      "Ram the\n",
      "0 0\n",
      "1 0\n",
      "(2, 768)\n",
      "the student newspaper at Fordham\n",
      "0 0\n",
      "1 0\n",
      "2 0\n",
      "3 0\n",
      "4 0\n",
      "(5, 768)\n",
      "Fordham University\n",
      "0 0\n",
      "1 0\n",
      "(2, 768)\n",
      "And one day I’m walking toward the campus center\n",
      "0 1\n",
      "1 1\n",
      "2 1\n",
      "3 1\n",
      "4 1\n",
      "5 1\n",
      "6 1\n",
      "7 1\n",
      "8 1\n",
      "9 1\n",
      "10 1\n",
      "(11, 768)\n",
      "center and out comes the elusive\n",
      "0 1\n",
      "1 1\n",
      "2 1\n",
      "3 1\n",
      "4 1\n",
      "5 1\n",
      "(6, 768)\n",
      "elusive Dean McGowan\n",
      "0 1\n",
      "1 1\n",
      "2 1\n",
      "3 1\n",
      "4 1\n",
      "(5, 768)\n",
      "McGowan architect of a\n",
      "0 1\n",
      "1 1\n",
      "2 1\n",
      "3 1\n",
      "4 1\n",
      "5 1\n",
      "(6, 768)\n",
      "policy to replace\n",
      "0 1\n",
      "1 1\n",
      "2 1\n",
      "(3, 768)\n",
      "replace Fordham’s traditionally\n",
      "0 1\n",
      "1 1\n",
      "2 1\n",
      "3 1\n",
      "4 1\n",
      "(5, 768)\n",
      "traditionally working to middle class\n",
      "0 1\n",
      "1 1\n",
      "2 1\n",
      "3 1\n",
      "4 1\n",
      "(5, 768)\n",
      "class students with\n",
      "0 1\n",
      "1 1\n",
      "2 1\n",
      "(3, 768)\n",
      "with wealthier more prestigious\n",
      "0 1\n",
      "1 1\n",
      "2 1\n",
      "3 1\n",
      "4 1\n",
      "(5, 768)\n",
      "prestigious ones\n",
      "0 1\n",
      "1 1\n",
      "(2, 768)\n",
      "So I whip out my notebook and I go up\n",
      "0 2\n",
      "1 2\n",
      "2 2\n",
      "3 2\n",
      "4 2\n",
      "5 2\n",
      "6 2\n",
      "7 2\n",
      "8 2\n",
      "9 2\n",
      "(10, 768)\n",
      "up to him and I say Dean McGowan\n",
      "0 2\n",
      "1 2\n",
      "2 2\n",
      "3 2\n",
      "4 2\n",
      "5 2\n",
      "6 2\n",
      "7 2\n",
      "8 2\n",
      "9 2\n",
      "(10, 768)\n",
      "is it true that Fordham\n",
      "0 2\n",
      "1 2\n",
      "2 2\n",
      "3 2\n",
      "4 2\n",
      "(5, 768)\n",
      "Fordham University plans to raise tuition\n",
      "0 2\n",
      "1 2\n",
      "2 2\n",
      "3 2\n",
      "4 2\n",
      "5 2\n",
      "(6, 768)\n",
      "tuition substantially above the inflation\n",
      "0 2\n",
      "1 2\n",
      "2 2\n",
      "3 2\n",
      "4 2\n",
      "(5, 768)\n",
      "inflation rate and if so\n",
      "0 2\n",
      "1 2\n",
      "2 2\n",
      "3 2\n",
      "4 2\n",
      "(5, 768)\n",
      "wouldn’t that be a betrayal of its mission\n",
      "0 2\n",
      "1 2\n",
      "2 2\n",
      "3 2\n",
      "4 2\n",
      "5 2\n",
      "6 2\n",
      "7 2\n",
      "8 2\n",
      "9 2\n",
      "(10, 768)\n",
      "mission\n",
      "0 2\n",
      "(1, 768)\n",
      "And he stops\n",
      "0 2\n",
      "1 2\n",
      "2 2\n",
      "(3, 768)\n",
      "stops and looks at me\n",
      "0 2\n",
      "1 2\n",
      "2 2\n",
      "3 2\n",
      "4 2\n",
      "(5, 768)\n",
      "and he says\n",
      "0 2\n",
      "1 2\n",
      "2 2\n",
      "(3, 768)\n",
      "Listen up punk And right\n",
      "0 2\n",
      "1 2\n",
      "2 2\n",
      "3 3\n",
      "4 3\n",
      "(5, 768)\n",
      "right then there’s a blur\n",
      "0 3\n",
      "1 3\n",
      "2 3\n",
      "3 3\n",
      "4 3\n",
      "5 3\n",
      "6 3\n",
      "(7, 768)\n",
      "blur in the corner of my eye which becomes\n",
      "0 3\n",
      "1 3\n",
      "2 3\n",
      "3 3\n",
      "4 3\n",
      "5 3\n",
      "6 3\n",
      "7 3\n",
      "8 3\n",
      "(9, 768)\n",
      "becomes this figure\n",
      "0 3\n",
      "1 3\n",
      "2 3\n",
      "(3, 768)\n",
      "holding a cream pie which becomes\n",
      "0 3\n",
      "1 3\n",
      "2 3\n",
      "3 3\n",
      "4 3\n",
      "5 3\n",
      "(6, 768)\n",
      "becomes the guy standing\n",
      "0 3\n",
      "1 3\n",
      "2 3\n",
      "3 3\n",
      "(4, 768)\n",
      "standing next to me\n",
      "0 3\n",
      "1 3\n",
      "2 3\n",
      "3 3\n",
      "(4, 768)\n",
      "mashing a cream pie\n",
      "0 3\n",
      "1 3\n",
      "2 3\n",
      "3 3\n",
      "4 3\n",
      "(5, 768)\n",
      "pie into Dean McGowan’s face\n",
      "0 3\n",
      "1 3\n",
      "2 3\n",
      "3 3\n",
      "4 3\n",
      "5 3\n",
      "6 3\n",
      "7 3\n",
      "8 3\n",
      "(9, 768)\n",
      "face\n",
      "0 3\n",
      "(1, 768)\n",
      "And then runs away\n",
      "0 4\n",
      "1 4\n",
      "2 4\n",
      "3 4\n",
      "(4, 768)\n",
      "And the Dean\n",
      "0 5\n",
      "1 5\n",
      "2 5\n",
      "(3, 768)\n",
      "Dean is covered\n",
      "0 5\n",
      "1 5\n",
      "2 5\n",
      "(3, 768)\n",
      "covered with cream\n",
      "0 5\n",
      "1 5\n",
      "2 5\n",
      "(3, 768)\n",
      "So I\n",
      "0 6\n",
      "1 6\n",
      "(2, 768)\n",
      "I give him a moment\n",
      "0 6\n",
      "1 6\n",
      "2 6\n",
      "3 6\n",
      "4 6\n",
      "(5, 768)\n",
      "and then I say Dean McGowan would you care\n",
      "0 6\n",
      "1 6\n",
      "2 6\n",
      "3 6\n",
      "4 6\n",
      "5 6\n",
      "6 6\n",
      "7 6\n",
      "8 6\n",
      "9 6\n",
      "10 6\n",
      "(11, 768)\n",
      "care to comment on this latest attack\n",
      "0 6\n",
      "1 6\n",
      "2 6\n",
      "3 7\n",
      "4 7\n",
      "5 7\n",
      "6 7\n",
      "(7, 768)\n",
      "attack And\n",
      "0 7\n",
      "1 7\n",
      "(2, 768)\n",
      "the Dean says Yes\n",
      "0 7\n",
      "1 7\n",
      "2 7\n",
      "3 7\n",
      "(4, 768)\n",
      "Yes I would care to comment\n",
      "0 7\n",
      "1 7\n",
      "2 7\n",
      "3 7\n",
      "4 7\n",
      "5 7\n",
      "(6, 768)\n",
      "comment Fuck you\n",
      "0 7\n",
      "1 7\n",
      "2 7\n",
      "(3, 768)\n",
      "you\n",
      "0 7\n",
      "(1, 768)\n",
      "So I race back to\n",
      "0 8\n",
      "1 8\n",
      "2 8\n",
      "3 8\n",
      "4 8\n",
      "(5, 768)\n",
      "to the newsroom with my scoop\n",
      "0 8\n",
      "1 8\n",
      "2 8\n",
      "3 8\n",
      "4 8\n",
      "5 8\n",
      "6 8\n",
      "(7, 768)\n",
      "and I find the editor\n",
      "0 8\n",
      "1 8\n",
      "2 8\n",
      "3 8\n",
      "4 8\n",
      "(5, 768)\n",
      "editor Jim Dwyer who’s a senior\n",
      "0 8\n",
      "1 8\n",
      "2 8\n",
      "3 8\n",
      "4 8\n",
      "5 8\n",
      "6 8\n",
      "7 8\n",
      "(8, 768)\n",
      "senior and he will go\n",
      "0 8\n",
      "1 8\n",
      "2 8\n",
      "3 8\n",
      "4 8\n",
      "(5, 768)\n",
      "go on to win a Pulitzer Prize that is\n",
      "0 8\n",
      "1 8\n",
      "2 8\n",
      "3 8\n",
      "4 8\n",
      "5 8\n",
      "6 8\n",
      "7 8\n",
      "8 8\n",
      "(9, 768)\n",
      "is true\n",
      "0 8\n",
      "1 8\n",
      "(2, 768)\n",
      "and he’s a big guy I pitch\n",
      "0 8\n",
      "1 8\n",
      "2 8\n",
      "3 8\n",
      "4 8\n",
      "5 8\n",
      "6 8\n",
      "7 9\n",
      "8 9\n",
      "(9, 768)\n",
      "pitch him my story I tell him what I’ve seen\n",
      "0 9\n",
      "1 9\n",
      "2 9\n",
      "3 9\n",
      "4 9\n",
      "5 9\n",
      "6 9\n",
      "7 9\n",
      "8 9\n",
      "9 9\n",
      "10 9\n",
      "11 9\n",
      "(12, 768)\n",
      "seen and he says\n",
      "0 9\n",
      "1 9\n",
      "2 9\n",
      "3 9\n",
      "(4, 768)\n",
      "Dean McGowan\n",
      "0 9\n",
      "1 9\n",
      "2 9\n",
      "3 9\n",
      "(4, 768)\n",
      "McGowan that guy’s a\n",
      "0 9\n",
      "1 9\n",
      "2 9\n",
      "3 9\n",
      "4 9\n",
      "5 9\n",
      "6 9\n",
      "7 9\n",
      "(8, 768)\n",
      "a dick Write it up\n",
      "0 9\n",
      "1 9\n",
      "2 10\n",
      "3 10\n",
      "4 10\n",
      "(5, 768)\n",
      "So I’m banging\n",
      "0 11\n",
      "1 11\n",
      "2 11\n",
      "3 11\n",
      "4 11\n",
      "(5, 768)\n",
      "banging out my story and I know it’s good\n",
      "0 11\n",
      "1 11\n",
      "2 11\n",
      "3 11\n",
      "4 11\n",
      "5 11\n",
      "6 11\n",
      "7 11\n",
      "8 11\n",
      "9 11\n",
      "10 11\n",
      "(11, 768)\n",
      "good and then I start to\n",
      "0 11\n",
      "1 11\n",
      "2 11\n",
      "3 11\n",
      "4 11\n",
      "5 11\n",
      "(6, 768)\n",
      "to make it better\n",
      "0 11\n",
      "1 11\n",
      "2 11\n",
      "3 11\n",
      "(4, 768)\n",
      "better\n",
      "0 11\n",
      "(1, 768)\n",
      "by adding an element of\n",
      "0 11\n",
      "1 11\n",
      "2 11\n",
      "3 11\n",
      "4 11\n",
      "(5, 768)\n",
      "of embellishment\n",
      "0 11\n",
      "1 11\n",
      "2 11\n",
      "3 11\n",
      "(4, 768)\n",
      "Reporters call this\n",
      "0 12\n",
      "1 12\n",
      "2 12\n",
      "(3, 768)\n",
      "this making shit\n",
      "0 12\n",
      "1 12\n",
      "2 12\n",
      "(3, 768)\n",
      "shit up\n",
      "0 12\n",
      "1 12\n",
      "(2, 768)\n",
      "And they recommend\n",
      "0 13\n",
      "1 13\n",
      "2 13\n",
      "(3, 768)\n",
      "recommend against crossing that line\n",
      "0 13\n",
      "1 13\n",
      "2 13\n",
      "3 13\n",
      "4 13\n",
      "(5, 768)\n",
      "line\n",
      "0 13\n",
      "(1, 768)\n",
      "But I had just seen\n",
      "0 14\n",
      "1 14\n",
      "2 14\n",
      "3 14\n",
      "4 14\n",
      "(5, 768)\n",
      "seen the line crossed\n",
      "0 14\n",
      "1 14\n",
      "2 14\n",
      "3 14\n",
      "(4, 768)\n",
      "crossed between a high powered dean\n",
      "0 14\n",
      "1 14\n",
      "2 14\n",
      "3 14\n",
      "4 14\n",
      "5 14\n",
      "(6, 768)\n",
      "dean and assault with a pastry\n",
      "0 14\n",
      "1 14\n",
      "2 14\n",
      "3 14\n",
      "4 14\n",
      "5 14\n",
      "(6, 768)\n",
      "pastry and I\n",
      "0 14\n",
      "1 14\n",
      "2 14\n",
      "(3, 768)\n",
      "I kind of liked it\n",
      "0 14\n",
      "1 14\n",
      "2 14\n",
      "3 14\n",
      "4 14\n",
      "(5, 768)\n",
      "So the first I did\n",
      "0 15\n",
      "1 15\n",
      "2 15\n",
      "3 15\n",
      "4 15\n",
      "(5, 768)\n",
      "was I gave the figure a name\n",
      "0 15\n",
      "1 15\n",
      "2 15\n",
      "3 15\n",
      "4 15\n",
      "5 15\n",
      "6 15\n",
      "(7, 768)\n",
      "name I called him Pie Man\n",
      "0 15\n",
      "1 16\n",
      "2 16\n",
      "3 16\n",
      "4 16\n",
      "5 16\n",
      "(6, 768)\n",
      "Man capital P capital M\n",
      "0 16\n",
      "1 16\n",
      "2 16\n",
      "3 16\n",
      "4 16\n",
      "(5, 768)\n",
      "M And I described\n",
      "0 16\n",
      "1 17\n",
      "2 17\n",
      "3 17\n",
      "(4, 768)\n",
      "described him as a cape wearing masked\n",
      "0 17\n",
      "1 17\n",
      "2 17\n",
      "3 17\n",
      "4 17\n",
      "5 17\n",
      "6 17\n",
      "(7, 768)\n",
      "masked avenger\n",
      "0 17\n",
      "1 17\n",
      "2 17\n",
      "(3, 768)\n",
      "Though in fact he’d been\n",
      "0 18\n",
      "1 18\n",
      "2 18\n",
      "3 18\n",
      "4 18\n",
      "5 18\n",
      "6 18\n",
      "(7, 768)\n",
      "been capeless\n",
      "0 18\n",
      "1 18\n",
      "2 18\n",
      "(3, 768)\n",
      "And said that as he fled the scene\n",
      "0 19\n",
      "1 19\n",
      "2 19\n",
      "3 19\n",
      "4 19\n",
      "5 19\n",
      "6 19\n",
      "7 19\n",
      "(8, 768)\n",
      "scene he clicked his heels\n",
      "0 19\n",
      "1 19\n",
      "2 19\n",
      "3 19\n",
      "4 19\n",
      "(5, 768)\n",
      "heels in rakish glee\n",
      "0 19\n",
      "1 19\n",
      "2 19\n",
      "3 19\n",
      "4 19\n",
      "5 19\n",
      "(6, 768)\n",
      "And I gave\n",
      "0 20\n",
      "1 20\n",
      "2 20\n",
      "(3, 768)\n",
      "gave him a catchphrase\n",
      "0 20\n",
      "1 20\n",
      "2 20\n",
      "3 20\n",
      "4 20\n",
      "5 20\n",
      "(6, 768)\n",
      "in Latin\n",
      "0 20\n",
      "1 20\n",
      "(2, 768)\n",
      "I said that he cried out\n",
      "0 21\n",
      "1 21\n",
      "2 21\n",
      "3 21\n",
      "4 21\n",
      "5 21\n",
      "(6, 768)\n",
      "Ego sum\n",
      "0 21\n",
      "1 21\n",
      "(2, 768)\n",
      "non an bestia\n",
      "0 21\n",
      "1 21\n",
      "2 21\n",
      "3 21\n",
      "(4, 768)\n",
      "bestia\n",
      "0 21\n",
      "1 21\n",
      "(2, 768)\n",
      "which means I am\n",
      "0 21\n",
      "1 21\n",
      "2 21\n",
      "3 21\n",
      "(4, 768)\n",
      "am not an animal\n",
      "0 21\n",
      "1 21\n",
      "2 21\n",
      "3 21\n",
      "(4, 768)\n",
      "animal\n",
      "0 21\n",
      "(1, 768)\n",
      "Which makes no sense\n",
      "0 22\n",
      "1 22\n",
      "2 22\n",
      "3 22\n",
      "(4, 768)\n",
      "sense\n",
      "0 22\n",
      "(1, 768)\n",
      "I needed something\n",
      "0 23\n",
      "1 23\n",
      "2 23\n",
      "(3, 768)\n",
      "something I’m Catholic\n",
      "0 23\n",
      "1 24\n",
      "2 24\n",
      "3 24\n",
      "4 24\n",
      "(5, 768)\n",
      "Catholic Latin just comes to me\n",
      "0 24\n",
      "1 24\n",
      "2 24\n",
      "3 24\n",
      "4 24\n",
      "5 24\n",
      "(6, 768)\n",
      "me\n",
      "0 24\n",
      "(1, 768)\n",
      "So I finish my story\n",
      "0 25\n",
      "1 25\n",
      "2 25\n",
      "3 25\n",
      "4 25\n",
      "(5, 768)\n",
      "and I hand it to Dwyer and he reads\n",
      "0 25\n",
      "1 25\n",
      "2 25\n",
      "3 25\n",
      "4 26\n",
      "5 26\n",
      "6 26\n",
      "7 26\n",
      "8 26\n",
      "(9, 768)\n",
      "reads and he says Pie\n",
      "0 26\n",
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      "2 26\n",
      "3 26\n",
      "4 26\n",
      "(5, 768)\n",
      "Pie Man\n",
      "0 26\n",
      "1 26\n",
      "(2, 768)\n",
      "I love it Page one\n",
      "0 26\n",
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      "2 26\n",
      "3 26\n",
      "4 26\n",
      "(5, 768)\n",
      "And that’s how\n",
      "0 27\n",
      "1 27\n",
      "2 27\n",
      "3 27\n",
      "4 27\n",
      "(5, 768)\n",
      "how the first line got\n",
      "0 27\n",
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      "3 27\n",
      "4 27\n",
      "(5, 768)\n",
      "got crossed\n",
      "0 27\n",
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      "(2, 768)\n",
      "A few days later\n",
      "0 28\n",
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      "2 28\n",
      "3 28\n",
      "(4, 768)\n",
      "later I get a letter\n",
      "0 28\n",
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      "3 28\n",
      "4 28\n",
      "(5, 768)\n",
      "It says Dear Jim\n",
      "0 29\n",
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      "3 29\n",
      "(4, 768)\n",
      "Jim good story Nice\n",
      "0 29\n",
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      "2 29\n",
      "3 30\n",
      "(4, 768)\n",
      "Nice details\n",
      "0 30\n",
      "1 30\n",
      "(2, 768)\n",
      "If you want to see me again\n",
      "0 31\n",
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      "4 31\n",
      "5 31\n",
      "6 31\n",
      "(7, 768)\n",
      "again in action\n",
      "0 31\n",
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      "2 31\n",
      "(3, 768)\n",
      "be on the steps of Duane\n",
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      "(6, 768)\n",
      "Duane Library Tuesday\n",
      "0 31\n",
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      "(3, 768)\n",
      "at 3 o’clock\n",
      "0 31\n",
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      "4 31\n",
      "(5, 768)\n",
      "signed\n",
      "0 31\n",
      "(1, 768)\n",
      "Pie Man\n",
      "0 31\n",
      "1 31\n",
      "(2, 768)\n",
      "So I was\n",
      "0 32\n",
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      "2 32\n",
      "(3, 768)\n",
      "was there with\n",
      "0 32\n",
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      "(3, 768)\n",
      "with a photographer from the Ram\n",
      "0 32\n",
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      "3 32\n",
      "4 32\n",
      "5 32\n",
      "(6, 768)\n",
      "And sure enough\n",
      "0 33\n",
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      "2 33\n",
      "(3, 768)\n",
      "out comes Sheila Beale\n",
      "0 33\n",
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      "3 33\n",
      "(4, 768)\n",
      "student body president\n",
      "0 33\n",
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      "(3, 768)\n",
      "president And now Sheila\n",
      "0 33\n",
      "1 34\n",
      "2 34\n",
      "3 34\n",
      "(4, 768)\n",
      "Sheila was different from me\n",
      "0 34\n",
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      "2 34\n",
      "3 34\n",
      "4 34\n",
      "(5, 768)\n",
      "and all the other Fordham students\n",
      "0 34\n",
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      "3 34\n",
      "4 34\n",
      "5 34\n",
      "(6, 768)\n",
      "students who wore flannel shirts and\n",
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      "6 34\n",
      "7 34\n",
      "(8, 768)\n",
      "and worked part time jobs\n",
      "0 34\n",
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      "3 34\n",
      "4 34\n",
      "(5, 768)\n",
      "Sheila was\n",
      "0 35\n",
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      "(2, 768)\n",
      "well bred Sheila\n",
      "0 35\n",
      "1 35\n",
      "2 36\n",
      "(3, 768)\n",
      "Sheila had school\n",
      "0 36\n",
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      "2 36\n",
      "(3, 768)\n",
      "school spirit\n",
      "0 36\n",
      "1 36\n",
      "(2, 768)\n",
      "Sheila\n",
      "0 37\n",
      "(1, 768)\n",
      "Sheila was the kind of student that Dean McGown\n",
      "0 37\n",
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      "10 38\n",
      "(11, 768)\n",
      "McGown wanted more of\n",
      "0 38\n",
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      "3 38\n",
      "4 38\n",
      "5 38\n",
      "(6, 768)\n",
      "Although rumor\n",
      "0 38\n",
      "1 38\n",
      "(2, 768)\n",
      "rumor had it that he got plenty of\n",
      "0 38\n",
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      "3 38\n",
      "4 38\n",
      "5 38\n",
      "6 38\n",
      "7 38\n",
      "(8, 768)\n",
      "of her in his office on\n",
      "0 38\n",
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      "(6, 768)\n",
      "on his desk\n",
      "0 38\n",
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      "2 38\n",
      "(3, 768)\n",
      "But that’s\n",
      "0 39\n",
      "1 39\n",
      "2 39\n",
      "3 39\n",
      "(4, 768)\n",
      "that’s just a rumor please do not spread it\n",
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      "(11, 768)\n",
      "it outside this rumor\n",
      "0 40\n",
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      "2 40\n",
      "3 40\n",
      "(4, 768)\n",
      "I myself would never\n",
      "0 40\n",
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      "2 40\n",
      "3 40\n",
      "(4, 768)\n",
      "never say that\n",
      "0 40\n",
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      "2 40\n",
      "(3, 768)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "But the fact was Sheila had\n",
      "0 41\n",
      "1 41\n",
      "2 41\n",
      "3 41\n",
      "4 41\n",
      "5 41\n",
      "(6, 768)\n",
      "had collaborated with the Dean\n",
      "0 41\n",
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      "2 41\n",
      "3 41\n",
      "4 41\n",
      "(5, 768)\n",
      "Dean to ban outdoor\n",
      "0 41\n",
      "1 41\n",
      "2 41\n",
      "3 41\n",
      "(4, 768)\n",
      "outdoor drinking on campus\n",
      "0 41\n",
      "1 41\n",
      "2 41\n",
      "3 41\n",
      "(4, 768)\n",
      "The infamous no more\n",
      "0 42\n",
      "1 42\n",
      "2 42\n",
      "3 42\n",
      "(4, 768)\n",
      "more beer at barbecues rule That’s\n",
      "0 42\n",
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      "3 42\n",
      "4 42\n",
      "5 42\n",
      "6 43\n",
      "7 43\n",
      "8 43\n",
      "(9, 768)\n",
      "That’s right boo that rule\n",
      "0 43\n",
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      "3 43\n",
      "4 43\n",
      "5 43\n",
      "6 43\n",
      "(7, 768)\n",
      "Sheila thought drinking\n",
      "0 44\n",
      "1 44\n",
      "2 44\n",
      "(3, 768)\n",
      "drinking in public was in\n",
      "0 44\n",
      "1 44\n",
      "2 44\n",
      "3 44\n",
      "4 44\n",
      "(5, 768)\n",
      "in poor taste\n",
      "0 44\n",
      "1 44\n",
      "2 44\n",
      "(3, 768)\n",
      "I\n",
      "0 45\n",
      "(1, 768)\n",
      "I think you know what happened\n",
      "0 45\n",
      "1 45\n",
      "2 45\n",
      "3 45\n",
      "4 45\n",
      "5 45\n",
      "(6, 768)\n",
      "happened next\n",
      "0 45\n",
      "1 45\n",
      "(2, 768)\n",
      "Pie Man emerged\n",
      "0 46\n",
      "1 46\n",
      "2 46\n",
      "(3, 768)\n",
      "emerged from behind the\n",
      "0 46\n",
      "1 46\n",
      "2 46\n",
      "3 46\n",
      "(4, 768)\n",
      "the late night library drop box\n",
      "0 46\n",
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      "3 46\n",
      "4 46\n",
      "5 46\n",
      "(6, 768)\n",
      "box made his delivery\n",
      "0 46\n",
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      "2 46\n",
      "3 46\n",
      "(4, 768)\n",
      "delivery and fled away\n",
      "0 46\n",
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      "2 46\n",
      "3 46\n",
      "(4, 768)\n",
      "crying\n",
      "0 46\n",
      "(1, 768)\n",
      "Ego sum non\n",
      "0 46\n",
      "1 46\n",
      "2 46\n",
      "(3, 768)\n",
      "non an bestia\n",
      "0 46\n",
      "1 46\n",
      "2 46\n",
      "3 46\n",
      "(4, 768)\n",
      "Or at least\n",
      "0 47\n",
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      "2 47\n",
      "(3, 768)\n",
      "least that’s what it said in my story in the\n",
      "0 47\n",
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      "3 47\n",
      "4 47\n",
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      "11 47\n",
      "(12, 768)\n",
      "the newspaper the next day\n",
      "0 47\n",
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      "3 47\n",
      "4 47\n",
      "(5, 768)\n",
      "which ran with a\n",
      "0 47\n",
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      "2 47\n",
      "3 47\n",
      "(4, 768)\n",
      "a photo of him\n",
      "0 47\n",
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      "2 47\n",
      "3 47\n",
      "(4, 768)\n",
      "leaving the scene cape\n",
      "0 47\n",
      "1 47\n",
      "2 47\n",
      "3 47\n",
      "(4, 768)\n",
      "cape flowing behind him\n",
      "0 47\n",
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      "2 47\n",
      "3 47\n",
      "(4, 768)\n",
      "him doing this\n",
      "0 47\n",
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      "2 47\n",
      "(3, 768)\n",
      "And that’s\n",
      "0 48\n",
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      "3 48\n",
      "(4, 768)\n",
      "that’s what made him a sensation\n",
      "0 48\n",
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      "(8, 768)\n",
      "sensation on campus\n",
      "0 48\n",
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      "2 48\n",
      "(3, 768)\n",
      "People started dressing like him\n",
      "0 49\n",
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      "(5, 768)\n",
      "him and quoting him in class\n",
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      "(6, 768)\n",
      "class The Ram ran\n",
      "0 49\n",
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      "(4, 768)\n",
      "ran five major stories about Pie\n",
      "0 50\n",
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      "4 50\n",
      "5 50\n",
      "(6, 768)\n",
      "Pie Man all of them by me\n",
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      "(7, 768)\n",
      "me And\n",
      "0 50\n",
      "1 51\n",
      "(2, 768)\n",
      "And toward the end of this run I was out at a bar\n",
      "0 51\n",
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      "(13, 768)\n",
      "bar one night and I saw\n",
      "0 51\n",
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      "4 51\n",
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      "(6, 768)\n",
      "I came and I saw in the\n",
      "0 51\n",
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      "(7, 768)\n",
      "the corner\n",
      "0 51\n",
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      "(2, 768)\n",
      "Angela from my Brit\n",
      "0 51\n",
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      "(4, 768)\n",
      "Brit Lit class drinking\n",
      "0 51\n",
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      "3 51\n",
      "(4, 768)\n",
      "drinking with some friends And now Angela\n",
      "0 51\n",
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      "3 51\n",
      "4 52\n",
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      "(7, 768)\n",
      "Angela and I had been flirting\n",
      "0 52\n",
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      "5 52\n",
      "(6, 768)\n",
      "flirting for two months\n",
      "0 52\n",
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      "3 52\n",
      "(4, 768)\n",
      "Or I\n",
      "0 53\n",
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      "(2, 768)\n",
      "I had been flirting with her\n",
      "0 53\n",
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      "(6, 768)\n",
      "and with\n",
      "0 53\n",
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      "(2, 768)\n",
      "with such nuance that\n",
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      "(5, 768)\n",
      "that there was a question about whether she\n",
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      "(8, 768)\n",
      "she knew I existed\n",
      "0 53\n",
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      "3 53\n",
      "(4, 768)\n",
      "So I I saw her there\n",
      "0 54\n",
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      "4 54\n",
      "5 54\n",
      "(6, 768)\n",
      "there and made a mental note to do nothing about\n",
      "0 54\n",
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      "(10, 768)\n",
      "about it\n",
      "0 54\n",
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      "(2, 768)\n",
      "And then I went to the bar\n",
      "0 55\n",
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      "(7, 768)\n",
      "bar to buy\n",
      "0 55\n",
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      "(3, 768)\n",
      "buy a round and I felt\n",
      "0 55\n",
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      "(6, 768)\n",
      "felt a tap on my shoulder\n",
      "0 55\n",
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      "4 55\n",
      "5 55\n",
      "(6, 768)\n",
      "And it was her\n",
      "0 56\n",
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      "(4, 768)\n",
      "her And\n",
      "0 56\n",
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      "(2, 768)\n",
      "And she said\n",
      "0 57\n",
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      "(3, 768)\n",
      "Jim we were just talking about\n",
      "0 57\n",
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      "5 57\n",
      "(6, 768)\n",
      "about how you always seem\n",
      "0 57\n",
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      "(5, 768)\n",
      "seem to know when\n",
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      "3 57\n",
      "(4, 768)\n",
      "when and where Pie\n",
      "0 57\n",
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      "2 57\n",
      "3 57\n",
      "(4, 768)\n",
      "Pie Man will strike\n",
      "0 57\n",
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      "2 57\n",
      "3 57\n",
      "(4, 768)\n",
      "And we were wondering\n",
      "0 58\n",
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      "3 58\n",
      "(4, 768)\n",
      "wondering are\n",
      "0 58\n",
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      "(2, 768)\n",
      "are you Pie Man\n",
      "0 58\n",
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      "(4, 768)\n",
      "Man And\n",
      "0 58\n",
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      "(2, 768)\n",
      "I\n",
      "0 58\n",
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      "I knew by the way she said it I\n",
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      "(9, 768)\n",
      "I knew that\n",
      "0 58\n",
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      "2 58\n",
      "(3, 768)\n",
      "that if I said yes she would have sex\n",
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      "(9, 768)\n",
      "sex with me\n",
      "0 58\n",
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      "(3, 768)\n",
      "And wasn’t\n",
      "0 59\n",
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      "3 59\n",
      "(4, 768)\n",
      "wasn’t I really Pie Man\n",
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      "Man\n",
      "0 59\n",
      "(1, 768)\n",
      "For having\n",
      "0 59\n",
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      "(2, 768)\n",
      "having brought him into being\n",
      "0 59\n",
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      "(5, 768)\n",
      "being Didn’t she only know\n",
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      "know about him through me\n",
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      "(5, 768)\n",
      "But she had asked me a straightforward\n",
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      "(7, 768)\n",
      "straightforward question that came with\n",
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      "(5, 768)\n",
      "with a straightforward answer In fact\n",
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      "3 60\n",
      "4 61\n",
      "5 61\n",
      "(6, 768)\n",
      "fact I wasn’t Pie\n",
      "0 61\n",
      "1 61\n",
      "2 61\n",
      "3 61\n",
      "4 61\n",
      "5 61\n",
      "(6, 768)\n",
      "Pie Man As far as I knew I had never\n",
      "0 61\n",
      "1 61\n",
      "2 62\n",
      "3 62\n",
      "4 62\n",
      "5 62\n",
      "6 62\n",
      "7 62\n",
      "8 62\n",
      "9 62\n",
      "(10, 768)\n",
      "never seen the guy\n",
      "0 62\n",
      "1 62\n",
      "2 62\n",
      "3 62\n",
      "(4, 768)\n",
      "guy out of costume\n",
      "0 62\n",
      "1 62\n",
      "2 62\n",
      "3 62\n",
      "(4, 768)\n",
      "So I looked at her and I\n",
      "0 63\n",
      "1 63\n",
      "2 63\n",
      "3 63\n",
      "4 63\n",
      "5 63\n",
      "6 63\n",
      "(7, 768)\n",
      "I said\n",
      "0 63\n",
      "1 63\n",
      "(2, 768)\n",
      "Yes\n",
      "0 63\n",
      "(1, 768)\n",
      "Yes Angela\n",
      "0 63\n",
      "1 63\n",
      "(2, 768)\n",
      "I am Pie Man\n",
      "0 64\n",
      "1 64\n",
      "2 64\n",
      "3 64\n",
      "(4, 768)\n",
      "And she said Oh good\n",
      "0 65\n",
      "1 65\n",
      "2 65\n",
      "3 65\n",
      "4 65\n",
      "(5, 768)\n",
      "Now buy me a beer\n",
      "0 66\n",
      "1 66\n",
      "2 66\n",
      "3 66\n",
      "4 66\n",
      "(5, 768)\n",
      "beer and tell me all\n",
      "0 66\n",
      "1 66\n",
      "2 66\n",
      "3 66\n",
      "4 66\n",
      "(5, 768)\n",
      "all about it\n",
      "0 66\n",
      "1 66\n",
      "2 66\n",
      "(3, 768)\n"
     ]
    }
   ],
   "source": [
    "all_tr_features = []\n",
    "all_tr_wordwise_features = []\n",
    "sent_cnt = 0  \n",
    "sent_word_cnt = 0\n",
    "sent = sentences[sent_cnt].strip()\n",
    "sent_enc = tokenizer.encode(sent)\n",
    "sent_size = len(sent_enc)-2\n",
    "\n",
    "tr_cnt = 0 \n",
    "tr_non_empty_cnt = 0\n",
    "for tr in tr_data:\n",
    "  tr_cnt += 1\n",
    "  if(tr != ''):\n",
    "    tr_wordwise_feat = []\n",
    "    print(tr)\n",
    "    tr_non_empty_cnt += 1\n",
    "    tr_feat = np.zeros(768)\n",
    "    tr_enc = tokenizer.encode(tr)\n",
    "    tr_size = len(tr_enc)-2\n",
    "\n",
    "    for i in range(0,tr_size):\n",
    "      print(i,sent_cnt)\n",
    "      tr_feat = np.add(tr_feat,sentences_features[sent_cnt][sent_word_cnt])\n",
    "      tr_wordwise_feat.append(sentences_features[sent_cnt][sent_word_cnt])\n",
    "      sent_word_cnt += 1\n",
    "\n",
    "      if(sent_word_cnt == sent_size):\n",
    "        sent_cnt+=1\n",
    "        sent_word_cnt = 0\n",
    "        sent = sentences[sent_cnt].strip()\n",
    "        sent_enc = tokenizer.encode(sent)\n",
    "        sent_size = len(sent_enc)-2\n",
    "    \n",
    "\n",
    "    if(tr_cnt < len(tr_data) and  tr.strip().split(' ')[-1] == tr_data[tr_cnt].strip().split(' ')[0]):\n",
    "      tokens_size = len(tokenizer.encode(tr.strip().split(' ')[-1]))-2\n",
    "      for i in range(0,tokens_size):\n",
    "        if(sent_word_cnt == 0):\n",
    "          sent_cnt-=1\n",
    "          sent = sentences[sent_cnt].strip()\n",
    "          sent_enc = tokenizer.encode(sent)\n",
    "          sent_size = len(sent_enc)-2\n",
    "          sent_word_cnt = sent_size-1\n",
    "        else:\n",
    "          sent_word_cnt -= 1\n",
    "\n",
    "    tr_feat = np.divide(tr_feat,tr_size)\n",
    "    all_tr_features.append(tr_feat)\n",
    "    tr_wordwise_feat = np.array(tr_wordwise_feat)\n",
    "    all_tr_wordwise_features.append(tr_wordwise_feat)\n",
    "    print(tr_wordwise_feat.shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "704IlNjfaHfH",
    "outputId": "6cf26513-38c5-40cd-e29f-2ef647104178"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-16-298401256a90>:2: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  all_tr_wordwise_features = np.array(all_tr_wordwise_features)\n"
     ]
    }
   ],
   "source": [
    "all_tr_features = np.array(all_tr_features)\n",
    "all_tr_wordwise_features = np.array(all_tr_wordwise_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3kDlbFWUbafG",
    "outputId": "900052d7-151f-4fe6-9f8e-1f4dba32dd87"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(259,)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_tr_features.shape\n",
    "all_tr_wordwise_features.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "V6Ucg6vJcDCW",
    "outputId": "2765fd17-e173-482a-9c93-c7117d4ef6ae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "259"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tr_non_empty_cnt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "t4rOI4EUeK_R"
   },
   "outputs": [],
   "source": [
    "non_empty_indices = []\n",
    "for i in range(0,len(tr_data)):\n",
    "  if(tr_data[i] == ''):\n",
    "    continue\n",
    "  else:\n",
    "    non_empty_indices.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "I-TrypG-edSx"
   },
   "outputs": [],
   "source": [
    "non_empty_indices = np.array(non_empty_indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "rKp-HrZT6UKf"
   },
   "outputs": [],
   "source": [
    "all_tr_wordwise_features_new = []\n",
    "tr_cnt = 0\n",
    "for tr in tr_data:\n",
    "  if(tr != ''):\n",
    "    tr_cnt += 1\n",
    "    cnt = 0\n",
    "    tr_feat = []\n",
    "    tr_words = tr.strip().split(' ')\n",
    "    tr_words_len = len(tr_words)\n",
    "\n",
    "    for i in range(0,tr_words_len):\n",
    "      enc = len(tokenizer.encode(tr_words[i]))-2\n",
    "      word_feat = np.zeros(768)\n",
    "      for j in range(0,enc):\n",
    "        word_feat = np.add(word_feat,all_tr_wordwise_features[tr_cnt-1][cnt])\n",
    "        cnt+=1\n",
    "      word_feat = np.divide(word_feat,enc)\n",
    "      tr_feat.append(word_feat)\n",
    "    \n",
    "    all_tr_wordwise_features_new.append(np.array(tr_feat))"
   ]
  },
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   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
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    "id": "Td-Lv0up6UVs",
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     "text": [
      "<ipython-input-23-ba8fe846053a>:1: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  all_tr_wordwise_features_new = np.array(all_tr_wordwise_features_new)\n"
     ]
    }
   ],
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    "all_tr_wordwise_features_new = np.array(all_tr_wordwise_features_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 768)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_tr_wordwise_features_new[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "dpk-fZWEefNx"
   },
   "outputs": [],
   "source": [
    "data = {'transcript': transcript,\n",
    "        'sentences': sentences,\n",
    "        'tr_data': tr_data,\n",
    "        'sentence_features': sentences_features,\n",
    "        'tr_features': all_tr_features,\n",
    "        'tr_wordwise_features': all_tr_wordwise_features_new,\n",
    "        'non_empty_tr_indices': non_empty_indices \n",
    "        }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "hof6qpIlfTz9"
   },
   "outputs": [],
   "source": [
    "f = open('pieman_data_shallowsyntax.npy','wb')\n",
    "np.save(f,data)\n",
    "f.close()"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "cow22pU56yCX"
   },
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   "source": []
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