4_Train_Test.ipynb

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   "source": [
    "# End to end process for adding both entity ruler and word vectors to an NER model\n",
    "1. Document cleaning and splitting the corpus into test and train sets\n",
    "2. Build word vectors\n",
    "3. Build training data with entity ruler and split into train and validation data\n",
    "4. Add word vectors to model, run"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25b74dc1-b409-4447-94c6-55c9d62dcaa3",
   "metadata": {},
   "source": [
    "## Notebook 4\n",
    "- Add word vectors to model\n",
    "- Train model\n",
    "- Test model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9933d1b5-0105-4695-810d-588f8769e536",
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   "outputs": [],
   "source": [
    "#load models\n",
    "import spacy\n",
    "\n",
    "#load test data\n",
    "import json\n",
    "\n",
    "#review test data\n",
    "from spacy import displacy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74679454-3fc8-4e2b-9f21-3738ef6a74ea",
   "metadata": {},
   "source": [
    "### Load word vectors into spaCy model\n",
    "- The train and valid data can be the same from the other NER set up\n",
    "- python3 -m spacy init vectors en word_vectors/word2vec_sw_word_vec_1.txt models/01 --name sw_word_vec_1\n",
    "- python3 -m spacy init vectors 'lang' 'location of vectors txt' 'output model loc' --name 'vectorsmodel name'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fb167bd7-8989-4679-a5fd-fb9e355b5d29",
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   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "\u001b[38;5;4mℹ Creating blank nlp object for language 'en'\u001b[0m\n",
      "[2022-02-19 14:59:38,347] [INFO] Reading vectors from word_vectors/word2vec_sw_word_vec_3.txt\n",
      "4175it [00:00, 8516.18it/s]\n",
      "[2022-02-19 14:59:38,867] [INFO] Loaded vectors from word_vectors/word2vec_sw_word_vec_3.txt\n",
      "\u001b[38;5;2m✔ Successfully converted 4175 vectors\u001b[0m\n",
      "\u001b[38;5;2m✔ Saved nlp object with vectors to output directory. You can now use\n",
      "the path to it in your config as the 'vectors' setting in [initialize].\u001b[0m\n",
      "/Users/sarasharick/Documents/NER/NER_wv_labels_end2end/models/03\n"
     ]
    }
   ],
   "source": [
    "!python3 -m spacy init vectors en word_vectors/word2vec_sw_word_vec_3.txt models/03 --name sw_word_vec_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "51fc6ddf-c25b-48b8-a8a0-4908b3f86116",
   "metadata": {},
   "outputs": [],
   "source": [
    "#load vectors model\n",
    "nlp = spacy.load('models/03')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "419a704f-df08-4534-bc75-d6ff51fae038",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<spacy.pipeline.ner.EntityRecognizer at 0x12ea5f6f0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#add ner pipe\n",
    "nlp.add_pipe('ner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "03be439e-a918-4b0c-9851-b6a88466c2db",
   "metadata": {},
   "outputs": [],
   "source": [
    "#resave model\n",
    "nlp.to_disk('models/03')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b2f8c12-5b4f-4a35-9d82-7c5e10735139",
   "metadata": {},
   "source": [
    "### Train with new model\n",
    "- python3 -m spacy train models/01/config.cfg --output models/02 --paths.train data/train.spacy --paths.dev data/valid.spacy --paths.vectors models/01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "768abfbb-93df-4fd8-bebc-c29b8f581d5e",
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    {
     "name": "stdout",
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     "text": [
      "\u001b[38;5;4mℹ Saving to output directory: models/04\u001b[0m\n",
      "\u001b[38;5;4mℹ Using CPU\u001b[0m\n",
      "\u001b[1m\n",
      "=========================== Initializing pipeline ===========================\u001b[0m\n",
      "[2022-02-19 14:59:55,597] [INFO] Set up nlp object from config\n",
      "[2022-02-19 14:59:55,607] [INFO] Pipeline: ['ner']\n",
      "[2022-02-19 14:59:55,611] [INFO] Created vocabulary\n",
      "[2022-02-19 14:59:55,897] [INFO] Added vectors: models/03\n",
      "[2022-02-19 14:59:55,928] [INFO] Finished initializing nlp object\n",
      "[2022-02-19 14:59:59,966] [INFO] Initialized pipeline components: ['ner']\n",
      "\u001b[38;5;2m✔ Initialized pipeline\u001b[0m\n",
      "\u001b[1m\n",
      "============================= Training pipeline =============================\u001b[0m\n",
      "\u001b[38;5;4mℹ Pipeline: ['ner']\u001b[0m\n",
      "\u001b[38;5;4mℹ Initial learn rate: 0.001\u001b[0m\n",
      "E    #       LOSS NER  ENTS_F  ENTS_P  ENTS_R  SCORE \n",
      "---  ------  --------  ------  ------  ------  ------\n",
      "  0       0     74.88   29.38   23.90   38.13    0.29\n",
      "  0     200   2762.04   86.83   91.21   82.86    0.87\n",
      "  1     400   1260.51   89.62   90.43   88.81    0.90\n",
      "  2     600   1135.41   91.51   91.76   91.25    0.92\n",
      "  3     800    911.67   92.95   93.20   92.70    0.93\n",
      "  5    1000    825.82   93.24   93.77   92.71    0.93\n",
      "  7    1200    625.82   93.67   93.42   93.92    0.94\n",
      "  9    1400    533.36   94.02   94.64   93.40    0.94\n",
      " 12    1600    555.96   94.21   94.69   93.73    0.94\n",
      " 15    1800    378.82   93.87   93.57   94.18    0.94\n",
      " 20    2000    291.36   94.19   94.34   94.05    0.94\n",
      " 25    2200    236.02   94.32   94.64   94.01    0.94\n",
      " 31    2400    238.86   94.16   94.64   93.69    0.94\n",
      " 37    2600    200.14   94.23   93.84   94.62    0.94\n",
      " 43    2800    135.23   94.68   95.11   94.25    0.95\n",
      " 50    3000    185.71   94.25   94.78   93.73    0.94\n",
      " 56    3200    119.55   94.17   94.72   93.62    0.94\n",
      " 62    3400    146.96   94.66   95.09   94.23    0.95\n",
      " 68    3600    112.25   94.23   94.30   94.16    0.94\n",
      " 75    3800    129.48   94.02   94.31   93.73    0.94\n",
      " 81    4000    135.55   94.28   95.11   93.47    0.94\n",
      " 87    4200    114.56   94.20   94.73   93.68    0.94\n",
      " 93    4400    108.46   94.47   94.59   94.36    0.94\n",
      "\u001b[38;5;2m✔ Saved pipeline to output directory\u001b[0m\n",
      "models/04/model-last\n"
     ]
    }
   ],
   "source": [
    "!python3 -m spacy train models/03/config.cfg --output models/04 --paths.train data/train.spacy --paths.dev data/valid.spacy --paths.vectors models/03"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8043117f-5a1a-4ab2-bcd6-77495f0cbda5",
   "metadata": {},
   "source": [
    "### Best Model\n",
    "#### Performance - Model is chosen by highest F1 score\n",
    "- ents_f: 0.9467818334\n",
    "- ents_p: 0.9511102818\n",
    "- ents_r: 0.9424926036\n",
    "- ner_loss: 135.2285380244\n",
    "\n",
    "#### By ent type - Person\n",
    "- p: 0.9776397516\n",
    "- r: 0.9790992784\n",
    "- f: 0.9783689707"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a6ddd1c-006f-4f55-a62c-b7c96ab0d8f1",
   "metadata": {},
   "source": [
    "### Review model on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "84fbf4f0-f71a-4461-9122-acae1ffbd7eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(file):\n",
    "    with open(file, 'r', encoding='utf-8') as f:\n",
    "        data = json.load(f)\n",
    "    return(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f5de0b29-0764-453a-9807-f50b694684e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = load_data('data/sw_test_ner.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "18d55c50-cdb7-49d7-ae01-f03043f2baca",
   "metadata": {},
   "outputs": [],
   "source": [
    "trained_nlp = spacy.load('./models/04/model-best')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 130,
   "id": "1014f36d-2621-426b-8d35-5321fb3295c1",
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       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">vaguely remember \n",
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       "    Deputy\n",
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       "    Sen\n",
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       " Reiga \n",
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       " watched \n",
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      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">fuck wrong better fucking fix</div></span>"
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       "    Leia\n",
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       " dad come main room without anyone mentioning \n",
       "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Wedge\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
       "</mark>\n",
       " newly discovered logic agree use \n",
       "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Leia\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
       "</mark>\n",
       " date official date</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    {
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      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">Assuming tries hit anything \n",
       "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Wedge\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
       "</mark>\n",
       " thought</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">Even though kind discovered already anyway</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">pervasiveness yes function \n",
       "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Brianna\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
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       "</div></span>"
      ],
      "text/plain": [
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    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">Since \n",
       "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    today\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">DATE</span>\n",
       "</mark>\n",
       "</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
       "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Luke\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
       "</mark>\n",
       " waited \n",
       "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Hobbie\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
       "</mark>\n",
       " say</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
       "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Tycho\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
       "</mark>\n",
       " shook head</div></span>"
      ],
      "text/plain": [
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      ]
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     "metadata": {},
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     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">pulled stolen holocron backpack</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">also \n",
       "<mark class=\"entity\" style=\"background: #7aecec; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Sith\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">ORG</span>\n",
       "</mark>\n",
       " think \n",
       "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Sheev Palpatine\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
       "</mark>\n",
       " multiple clans \n",
       "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Dathomiri Nightsisters\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
       "</mark>\n",
       " Whills Massasii lots others</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Half\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">CARDINAL</span>\n",
       "</mark>\n",
       " speed</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">Maybe \n",
       "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Adan\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
       "</mark>\n",
       "</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\"></div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
       "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Tycho\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
       "</mark>\n",
       " saw coming</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">Like never close</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">short answer</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
       "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Luke\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
       "</mark>\n",
       " nodded started smile</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
       "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Brianna\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
       "</mark>\n",
       " laughed</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">Yeah look go see later</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">aware</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">Even \n",
       "<mark class=\"entity\" style=\"background: #bfe1d9; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    two hours\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">TIME</span>\n",
       "</mark>\n",
       " later way \n",
       "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Wes\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
       "</mark>\n",
       " still</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
       "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Tycho\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
       "</mark>\n",
       " stared</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">taken particular interest reading reporting going Praxis</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">working within \n",
       "<mark class=\"entity\" style=\"background: #e4e7d2; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    couple meters\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">QUANTITY</span>\n",
       "</mark>\n",
       "</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
       "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Wedge\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
       "</mark>\n",
       " also apartment</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">true</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">dinner plate</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">\n",
       "<mark class=\"entity\" style=\"background: #aa9cfc; padding: 0.45em 0.6em; margin: 0 0.25em; line-height: 1; border-radius: 0.35em;\">\n",
       "    Cami\n",
       "    <span style=\"font-size: 0.8em; font-weight: bold; line-height: 1; border-radius: 0.35em; vertical-align: middle; margin-left: 0.5rem\">PERSON</span>\n",
       "</mark>\n",
       "</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">ship rumbled finally jumped hyperspace</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">says</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\"></div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<span class=\"tex2jax_ignore\"><div class=\"entities\" style=\"line-height: 2.5; direction: ltr\">Feeling hopeless</div></span>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "test = test_data[1275:1325]\n",
    "\n",
    "for item in test:\n",
    "    doc = trained_nlp(item)\n",
    "    displacy.render(doc, style='ent')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7204aef-c9f8-449d-8ad8-d9f643ef0721",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb992c13-3b12-453e-8a90-7dd97ca768a3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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