part-4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "703ecc50-bc49-45e5-aae5-ca5bbca96706",
   "metadata": {},
   "source": [
    "# SpanMarker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8747827e-108c-458c-8ac1-1ea42d57ac78",
   "metadata": {},
   "outputs": [],
   "source": [
    "from span_marker import SpanMarkerModel\n",
    "\n",
    "model = SpanMarkerModel.from_pretrained(\"tomaarsen/span-marker-mbert-base-multinerd\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a96ad489-1c9f-4a5e-b2df-b3c43c5e3f89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.19 s, sys: 13.2 ms, total: 1.21 s\n",
      "Wall time: 1.22 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'span': 'Vincent D. Warmerdam',\n",
       "  'label': 'PER',\n",
       "  'score': 0.9990239143371582,\n",
       "  'char_start_index': 11,\n",
       "  'char_end_index': 31}]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time \n",
    "\n",
    "model.predict(\"My name is Vincent D. Warmerdam\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "305095d8-8c09-4c07-a708-68eb7b446871",
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "066d4d5d-1d03-4908-b99c-adb73646d5d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load(\"en_core_web_md\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fbeb9369-460e-4de6-b39b-b6624fe47fdd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 8.28 ms, sys: 67 µs, total: 8.35 ms\n",
      "Wall time: 7.58 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(Vincent D. Warmerdam,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "nlp(\"My name is Vincent D. Warmerdam\").ents"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "229dae4d",
   "metadata": {},
   "source": [
    "## spaCy-LLM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f0910c9a-995f-4e6d-80f2-c42fd165e982",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %pip install spacy-llm python-dotenv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "de22f5f7-cc1a-46d8-ae4e-ebd335cfc1ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-rw-rw- 1 codespace codespace 119 Apr 11 19:44 .env\n",
      "-rw-rw-rw- 1 codespace codespace 283 Apr 11 19:41 spacy-llm.cfg\n"
     ]
    }
   ],
   "source": [
    "! ls -lhat .env spacy-llm.cfg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "32d268c4-ddd7-421e-8c39-3c45af1db6dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(pizza, anchovis)\n"
     ]
    }
   ],
   "source": [
    "from spacy_llm.util import assemble\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "# Make sure the environment variables are loaded\n",
    "load_dotenv()\n",
    "\n",
    "# Assemble a spaCy pipeline from the config\n",
    "nlp = assemble(\"spacy-llm-adv.cfg\")\n",
    "\n",
    "# Use this pipeline as you would normally\n",
    "doc = nlp(\"I know of a great pizza recipe with anchovis.\")\n",
    "print(doc.ents) # (pizza, anchovis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f61744fa-5f46-4a30-b2c9-15f0d0e966f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "I know of a great pizza recipe with anchovis."
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "doc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "59990d05-e5e4-4109-89b3-8ed58b6e91af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You are an expert Named Entity Recognition (NER) system. Your task is to accept Text as input and extract named entities for the set of predefined entity labels.\n",
      "From the Text input provided, extract named entities for each label in the following format:\n",
      "\n",
      "DISH: <comma delimited list of strings>\n",
      "EQUIPMENT: <comma delimited list of strings>\n",
      "INGREDIENT: <comma delimited list of strings>\n",
      "\n",
      "Below are definitions of each label to help aid you in what kinds of named entities to extract for each label.\n",
      "Assume these definitions are written by an expert and follow them closely.\n",
      "\n",
      "DISH: Extract the name of a known dish.\n",
      "INGREDIENT: Extract the name of a cooking ingredient, including herbs and spices.\n",
      "EQUIPMENT: Extract any mention of cooking equipment. e.g. oven, cooking pot, grill\n",
      "\n",
      "\n",
      "Below are some examples (only use these as a guide):\n",
      "\n",
      "Text:\n",
      "'''\n",
      "You can't get a great chocolate flavor with carob.\n",
      "'''\n",
      "\n",
      "DISH: \n",
      "EQUIPMENT: \n",
      "\n",
      "Text:\n",
      "'''\n",
      "You can probably sand-blast it if it's an anodized aluminum pan.\n",
      "'''\n",
      "\n",
      "DISH: \n",
      "EQUIPMENT: anodized aluminum pan\n",
      "\n",
      "\n",
      "Here is the text that needs labeling:\n",
      "\n",
      "Text:\n",
      "'''\n",
      "I know of a great pizza recipe with anchovis.\n",
      "'''\n"
     ]
    }
   ],
   "source": [
    "print(doc.user_data['llm_io']['llm']['prompt'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c3367233-322b-4a05-a500-1cd5c271a612",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DISH: pizza\n",
      "EQUIPMENT: \n",
      "INGREDIENT: anchovis\n"
     ]
    }
   ],
   "source": [
    "print(doc.user_data['llm_io']['llm']['response'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ae6abcf-64d0-492f-bf0a-c290bddd750d",
   "metadata": {},
   "source": [
    "## Gliner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ad11314f-3df9-4eda-a28a-9ddc13c46d53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %pip install gliner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "27107cad-54c4-4b23-84ee-1bce99886f83",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gliner import GLiNER\n",
    "import srsly\n",
    "\n",
    "# Initialize GLiNER with the base model\n",
    "model = GLiNER.from_pretrained(\"urchade/gliner_small-v2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5bee4a7e-22bd-434f-b911-6bc1fdcc40f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Django app => python-tool\n",
      "HTMX => python-tool\n",
      "Django => python-tool\n",
      "HTMX => python-tool\n",
      "HTMX => python-tool\n",
      "Django => python-tool\n",
      "Python => python-tool\n",
      "Python => python-tool\n",
      "Python => python-tool\n",
      "Sentry => python-tool\n",
      "Sentry => python-tool\n",
      "Sentry-launch-week => python-tool\n",
      "live mic => python-tool\n",
      "Django => python-tool\n",
      "HTMX => python-tool\n",
      "Django => python-tool\n",
      "Django => python-tool\n",
      "Django => python-tool\n",
      "Django => python-tool\n",
      "HTMX => python-tool\n",
      "Django => python-tool\n",
      "HTMX => python-tool\n",
      "Django => python-tool\n",
      "Django five => python-tool\n",
      "library => python-tool\n",
      "Vue => python-tool\n",
      "HTMX => python-tool\n",
      "Django => python-tool\n",
      "Django => python-tool\n",
      "Django => python-tool\n",
      "PyCon => python-tool\n",
      "DjangoCon => python-tool\n",
      "PyCon => python-tool\n",
      "requests => python-tool\n",
      "requests => python-tool\n",
      "Python => python-tool\n",
      "Django => python-tool\n",
      "Python => python-tool\n",
      "go => python-tool\n",
      "Django => python-tool\n",
      "Python => python-tool\n",
      "Python => python-tool\n",
      "async => python-tool\n",
      "Django => python-tool\n",
      "fast API => python-tool\n",
      "fast API => python-tool\n",
      "Django => python-tool\n",
      "API => python-tool\n",
      "SQL alchemy => python-tool\n",
      "Django => python-tool\n",
      "Django => python-tool\n",
      "fast API => python-tool\n",
      "Django => python-tool\n",
      "Django Ninja => python-tool\n",
      "Django => python-tool\n",
      "Django => python-tool\n",
      "Django => python-tool\n",
      "admin => python-tool\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[16], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m labels \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpython-tool\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m srsly\u001b[38;5;241m.\u001b[39mread_jsonl(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpart-3/data/lines.jsonl\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[0;32m----> 4\u001b[0m     entities \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_entities\u001b[49m\u001b[43m(\u001b[49m\u001b[43mline\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtext\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      5\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m entity \u001b[38;5;129;01min\u001b[39;00m entities:\n\u001b[1;32m      6\u001b[0m         \u001b[38;5;28mprint\u001b[39m(entity[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m\"\u001b[39m], \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m=>\u001b[39m\u001b[38;5;124m\"\u001b[39m, entity[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlabel\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n",
      "File \u001b[0;32m/usr/local/python/3.10.13/lib/python3.10/site-packages/gliner/model.py:277\u001b[0m, in \u001b[0;36mGLiNER.predict_entities\u001b[0;34m(self, text, labels, flat_ner, threshold)\u001b[0m\n\u001b[1;32m    276\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict_entities\u001b[39m(\u001b[38;5;28mself\u001b[39m, text, labels, flat_ner\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, threshold\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m):\n\u001b[0;32m--> 277\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch_predict_entities\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflat_ner\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mflat_ner\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthreshold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mthreshold\u001b[49m\u001b[43m)\u001b[49m[\u001b[38;5;241m0\u001b[39m]\n",
      "File \u001b[0;32m/usr/local/python/3.10.13/lib/python3.10/site-packages/gliner/model.py:305\u001b[0m, in \u001b[0;36mGLiNER.batch_predict_entities\u001b[0;34m(self, texts, labels, flat_ner, threshold)\u001b[0m\n\u001b[1;32m    303\u001b[0m input_x \u001b[38;5;241m=\u001b[39m [{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtokenized_text\u001b[39m\u001b[38;5;124m\"\u001b[39m: tk, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mner\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mNone\u001b[39;00m} \u001b[38;5;28;01mfor\u001b[39;00m tk \u001b[38;5;129;01min\u001b[39;00m all_tokens]\n\u001b[1;32m    304\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcollate_fn(input_x, labels)\n\u001b[0;32m--> 305\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mflat_ner\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mflat_ner\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthreshold\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mthreshold\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    307\u001b[0m all_entities \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m    308\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, output \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(outputs):\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/utils/_contextlib.py:115\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m    113\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    114\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 115\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/python/3.10.13/lib/python3.10/site-packages/gliner/model.py:258\u001b[0m, in \u001b[0;36mGLiNER.predict\u001b[0;34m(self, x, flat_ner, threshold)\u001b[0m\n\u001b[1;32m    255\u001b[0m \u001b[38;5;129m@torch\u001b[39m\u001b[38;5;241m.\u001b[39mno_grad()\n\u001b[1;32m    256\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict\u001b[39m(\u001b[38;5;28mself\u001b[39m, x, flat_ner\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, threshold\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m):\n\u001b[1;32m    257\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39meval()\n\u001b[0;32m--> 258\u001b[0m     local_scores \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_score_eval\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparameters\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    259\u001b[0m     probs \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39msigmoid(local_scores)\n\u001b[1;32m    261\u001b[0m     spans \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m/usr/local/python/3.10.13/lib/python3.10/site-packages/gliner/model.py:249\u001b[0m, in \u001b[0;36mGLiNER.compute_score_eval\u001b[0;34m(self, x, device)\u001b[0m\n\u001b[1;32m    245\u001b[0m entity_type_rep \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprompt_rep_layer(entity_type_rep)  \u001b[38;5;66;03m# (batch_size, len_types, hidden_size)\u001b[39;00m\n\u001b[1;32m    247\u001b[0m word_rep \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrnn(word_rep, mask)\n\u001b[0;32m--> 249\u001b[0m span_rep \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mspan_rep_layer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mword_rep\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mspan_idx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    251\u001b[0m local_scores \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39meinsum(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mBLKD,BCD->BLKC\u001b[39m\u001b[38;5;124m\"\u001b[39m, span_rep, entity_type_rep)\n\u001b[1;32m    253\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m local_scores\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m/usr/local/python/3.10.13/lib/python3.10/site-packages/gliner/modules/span_rep.py:369\u001b[0m, in \u001b[0;36mSpanRepLayer.forward\u001b[0;34m(self, x, *args)\u001b[0m\n\u001b[1;32m    367\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x, \u001b[38;5;241m*\u001b[39margs):\n\u001b[0;32m--> 369\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mspan_rep_layer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m/usr/local/python/3.10.13/lib/python3.10/site-packages/gliner/modules/span_rep.py:291\u001b[0m, in \u001b[0;36mSpanMarkerV0.forward\u001b[0;34m(self, h, span_idx)\u001b[0m\n\u001b[1;32m    288\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, h: torch\u001b[38;5;241m.\u001b[39mTensor, span_idx: torch\u001b[38;5;241m.\u001b[39mTensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m torch\u001b[38;5;241m.\u001b[39mTensor:\n\u001b[1;32m    289\u001b[0m     B, L, D \u001b[38;5;241m=\u001b[39m h\u001b[38;5;241m.\u001b[39msize()\n\u001b[0;32m--> 291\u001b[0m     start_rep \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mproject_start\u001b[49m\u001b[43m(\u001b[49m\u001b[43mh\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    292\u001b[0m     end_rep \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mproject_end(h)\n\u001b[1;32m    294\u001b[0m     start_span_rep \u001b[38;5;241m=\u001b[39m extract_elements(start_rep, span_idx[:, :, \u001b[38;5;241m0\u001b[39m])\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/nn/modules/container.py:217\u001b[0m, in \u001b[0;36mSequential.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    215\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m--> 217\u001b[0m         \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    218\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28minput\u001b[39m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/torch/nn/modules/linear.py:116\u001b[0m, in \u001b[0;36mLinear.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 116\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "labels = [\"python-tool\"]\n",
    "\n",
    "for line in srsly.read_jsonl(\"part-3/data/lines.jsonl\"):\n",
    "    entities = model.predict_entities(line['text'], labels)\n",
    "    for entity in entities:\n",
    "        print(entity[\"text\"], \"=>\", entity[\"label\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ca34143c-3f7f-4b48-9f4a-5c860f66012a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe627397-65b5-4797-8aec-797b919d0f07",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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