{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transformer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Użyj transformeroewgo pipeline'u (https://huggingface.co/docs/transformers/main_classes/pipelines) do implementacji zadania rozpoznawania jednostek nazewniczych (NER) na zbiorze danych https://git.wmi.amu.edu.pl/kubapok/en-ner-conll-2003. \\\n",
    "Dokonaj ewaluacji za pomocą narzędzia GEval."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import bibliotek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from transformers import pipeline, AutoModelForTokenClassification, AutoTokenizer\n",
    "from tqdm.notebook import tqdm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wczytanie danych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_A_data = pd.read_csv(\"test-A/in.tsv\", sep=\"\\t\", header=None, names=[\"x\"])\n",
    "dev_0_data = pd.read_csv(\"dev-0/in.tsv\", sep=\"\\t\", header=None, names=[\"x\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ustawienie modelu, tokenizatora oraz pipeline'u"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at dbmdz/bert-large-cased-finetuned-conll03-english were not used when initializing BertForTokenClassification: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n",
      "- This IS expected if you are initializing BertForTokenClassification 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 BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "model = AutoModelForTokenClassification.from_pretrained(\"dbmdz/bert-large-cased-finetuned-conll03-english\")\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"google-bert/bert-base-cased\")\n",
    "recognizer = pipeline(\"ner\", model=model, tokenizer=tokenizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Funkcja naprawiająca zbiory przewidzianych tagów"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def correct_labels(data):\n",
    "    corrected_lines = []\n",
    "\n",
    "    for line in data:\n",
    "        corrected_line = []\n",
    "        previous_token = \"O\"\n",
    "\n",
    "        for token in line:\n",
    "            if (\n",
    "                token == \"I-ORG\"\n",
    "                and previous_token != \"B-ORG\"\n",
    "                and previous_token != \"I-ORG\"\n",
    "            ):\n",
    "                corrected_line.append(\"B-ORG\")\n",
    "            elif (\n",
    "                token == \"I-PER\"\n",
    "                and previous_token != \"B-PER\"\n",
    "                and previous_token != \"I-PER\"\n",
    "            ):\n",
    "                corrected_line.append(\"B-PER\")\n",
    "            elif (\n",
    "                token == \"I-LOC\"\n",
    "                and previous_token != \"B-LOC\"\n",
    "                and previous_token != \"I-LOC\"\n",
    "            ):\n",
    "                corrected_line.append(\"B-LOC\")\n",
    "            elif (\n",
    "                token == \"I-MISC\"\n",
    "                and previous_token != \"B-MISC\"\n",
    "                and previous_token != \"I-MISC\"\n",
    "            ):\n",
    "                corrected_line.append(\"B-MISC\")\n",
    "            else:\n",
    "                corrected_line.append(token)\n",
    "\n",
    "            previous_token = token\n",
    "\n",
    "        corrected_lines.append(corrected_line)\n",
    "\n",
    "    return corrected_lines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Funkcja przewidująca tagi NER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_ner_tags(data):\n",
    "    predictions = []\n",
    "    counter = 1\n",
    "    for line in data:\n",
    "        print(f'Predicting NER tags for line {counter}/{len(data)}... ', end='')\n",
    "        word_positions = []\n",
    "        position = 0\n",
    "        result = recognizer(line)\n",
    "        entity_dict = {res['start']: res['entity'] for res in result}\n",
    "\n",
    "        for word in line.split():\n",
    "            word_positions.append(position)\n",
    "            position += len(word) + 1\n",
    "        classified_words = []\n",
    "\n",
    "        for checked_position in word_positions:\n",
    "            entity = entity_dict.get(checked_position, \"O\")\n",
    "            classified_words.append(entity)\n",
    "\n",
    "        predictions.append(classified_words)\n",
    "        print('Done')\n",
    "        counter += 1\n",
    "    return correct_labels(predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Funkcja zapisująca wyniki"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_predictions(predictions, filename):\n",
    "    with open(filename, \"w\") as f:\n",
    "        for line in predictions:\n",
    "            f.write(\" \".join(line) + \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wyznaczenie tagów NER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction for dev-0 data\n",
      "Predicting NER tags for line 1/215... Done\n",
      "Predicting NER tags for line 2/215... Done\n",
      "Predicting NER tags for line 3/215... Done\n",
      "Predicting NER tags for line 4/215... Done\n",
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      "\n",
      "Prediction for test-A data\n",
      "Predicting NER tags for line 1/230... Done\n",
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     ]
    }
   ],
   "source": [
    "print(\"Prediction for dev-0 data\")\n",
    "dev_0_labels = predict_ner_tags(dev_0_data[\"x\"])\n",
    "\n",
    "print()\n",
    "\n",
    "print(\"Prediction for test-A data\")\n",
    "test_A_labels = predict_ner_tags(test_A_data[\"x\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zapis wyników do plików"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_predictions(dev_0_labels, \"dev-0/out.tsv\")\n",
    "save_predictions(test_A_labels, \"test-A/out.tsv\")"
   ]
  }
 ],
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