apply_transformer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "ybQwDcy_ILvi"
   },
   "outputs": [],
   "source": [
    "# torch libraries\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "# pytorch libraries\n",
    "import pytorch_lightning as pl\n",
    "from torchmetrics import F1Score\n",
    "from torchmetrics.functional import accuracy, auroc #F1Score #f1\n",
    "from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping\n",
    "from pytorch_lightning.loggers import TensorBoardLogger\n",
    "\n",
    "# transformers libraries\n",
    "from transformers import AutoTokenizer, DebertaV2Model, AdamW, get_linear_schedule_with_warmup\n",
    "\n",
    "from tqdm.auto import tqdm\n",
    "\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "LzsccF0-jp1j"
   },
   "outputs": [],
   "source": [
    "#set working directory to the downloaded folder\n",
    "import os\n",
    "os.chdir('/Users/au486628/Library/CloudStorage/GoogleDrive-tbs.widmann@gmail.com/My Drive/Work/Tools/political-moral-emotional-mDeBERTa')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "OKvNWFPdIXSE"
   },
   "outputs": [],
   "source": [
    "BERT_MODEL_NAME = \"microsoft/mdeberta-v3-base\"\n",
    "LABEL_COLUMNS = ['morality_binary', 'emotion_binary', 'positive_binary', 'negative_binary']\n",
    "\n",
    "class CrowdCodedTagger(pl.LightningModule):\n",
    "\n",
    "    def __init__(self, n_classes: int, n_training_steps=None, n_warmup_steps=None):\n",
    "        super().__init__()\n",
    "        self.bert = DebertaV2Model.from_pretrained(BERT_MODEL_NAME, return_dict=True)\n",
    "        self.classifier = nn.Linear(self.bert.config.hidden_size, n_classes)\n",
    "        self.n_training_steps = n_training_steps\n",
    "        self.n_warmup_steps = n_warmup_steps\n",
    "        self.criterion = nn.BCELoss()\n",
    "\n",
    "    def forward(self, input_ids, attention_mask, labels=None, token_type_ids=None):\n",
    "        output = self.bert(input_ids, attention_mask=attention_mask)\n",
    "        output = self.classifier(output.last_hidden_state[:, 0])\n",
    "        output = torch.sigmoid(output)\n",
    "        loss = 0\n",
    "        if labels is not None:\n",
    "            loss = self.criterion(output, labels)\n",
    "        return loss, output\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        input_ids = batch[\"input_ids\"]\n",
    "        attention_mask = batch[\"attention_mask\"]\n",
    "        labels = batch[\"labels\"]\n",
    "        loss, outputs = self(input_ids, attention_mask, labels)\n",
    "        self.log(\"train_loss\", loss, prog_bar=True, logger=True)\n",
    "        return {\"loss\": loss, \"predictions\": outputs, \"labels\": labels}\n",
    "\n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        input_ids = batch[\"input_ids\"]\n",
    "        attention_mask = batch[\"attention_mask\"]\n",
    "        labels = batch[\"labels\"]\n",
    "        loss, outputs = self(input_ids, attention_mask, labels)\n",
    "        self.log(\"val_loss\", loss, prog_bar=True, logger=True)\n",
    "        return loss\n",
    "\n",
    "    def test_step(self, batch, batch_idx):\n",
    "        input_ids = batch[\"input_ids\"]\n",
    "        attention_mask = batch[\"attention_mask\"]\n",
    "        labels = batch[\"labels\"]\n",
    "        loss, outputs = self(input_ids, attention_mask, labels)\n",
    "        self.log(\"test_loss\", loss, prog_bar=True, logger=True)\n",
    "        return loss\n",
    "\n",
    "    def training_epoch_end(self, outputs):\n",
    "\n",
    "        labels = []\n",
    "        predictions = []\n",
    "        for output in outputs:\n",
    "          for out_labels in output[\"labels\"].detach().cpu():\n",
    "            labels.append(out_labels)\n",
    "          for out_predictions in output[\"predictions\"].detach().cpu():\n",
    "            predictions.append(out_predictions)\n",
    "\n",
    "        labels = torch.stack(labels).int()\n",
    "        predictions = torch.stack(predictions)\n",
    "\n",
    "        for i, name in enumerate(LABEL_COLUMNS):\n",
    "          class_roc_auc = auroc(predictions[:, i], labels[:, i])\n",
    "          self.logger.experiment.add_scalar(f\"{name}_roc_auc/Train\", class_roc_auc, self.current_epoch)\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "\n",
    "      optimizer = AdamW(self.parameters(), lr=2e-5) #DEFINING LEARNING RATE\n",
    "\n",
    "      scheduler = get_linear_schedule_with_warmup(\n",
    "        optimizer,\n",
    "        num_warmup_steps=self.n_warmup_steps,\n",
    "        num_training_steps=self.n_training_steps\n",
    "      )\n",
    "\n",
    "      return dict(\n",
    "        optimizer=optimizer,\n",
    "        lr_scheduler=dict(\n",
    "          scheduler=scheduler,\n",
    "          interval='step'\n",
    "        )\n",
    "      )\n",
    "\n",
    "def process_dataframe_with_transformer(df):\n",
    "    model = CrowdCodedTagger(n_classes=4)\n",
    "    model.load_state_dict(torch.load(\"./model/pytorch_model.pt\"), strict = False)\n",
    "    model.to(\"cpu\") # model.to(\"cuda\")\n",
    "    model.eval()  # putting model into evaluation mode\n",
    "\n",
    "    tokenizer = AutoTokenizer.from_pretrained(BERT_MODEL_NAME)\n",
    "    batch_size = 8\n",
    "\n",
    "    def predict_labels(df):\n",
    "        input_text = df['sentence'].tolist()\n",
    "        num_inputs = len(input_text)\n",
    "        num_batches = (num_inputs - 1) // batch_size + 1\n",
    "        batched_input = [input_text[i * batch_size:(i + 1) * batch_size] for i in range(num_batches)]\n",
    "        batched_output = []\n",
    "\n",
    "        for i, batch in enumerate(tqdm(batched_input)):\n",
    "            encoded_input = tokenizer(batch, padding=True, truncation=True, max_length=512, return_tensors='pt')\n",
    "            outputs = model(**encoded_input.to(\"cpu\")) #outputs = model(**encoded_input.to(\"cuda\"))\n",
    "\n",
    "            tensor_values = outputs[1].tolist()\n",
    "            decimal_numbers = [[num for num in sublist] for sublist in tensor_values]\n",
    "            output_df = pd.DataFrame(decimal_numbers, columns=LABEL_COLUMNS)\n",
    "            threshold = 0.5\n",
    "            threshold_fn = lambda x: 1 if x >= threshold else 0\n",
    "            output_df = output_df.applymap(threshold_fn)\n",
    "            input_df = df.iloc[i * batch_size:(i + 1) * batch_size].reset_index(drop=True)\n",
    "            output_df = pd.concat([input_df, output_df], axis=1)\n",
    "\n",
    "            batched_output.append(output_df)\n",
    "\n",
    "        output_df = pd.concat(batched_output, ignore_index=True)\n",
    "        return output_df\n",
    "\n",
    "    processed_df = predict_labels(df)\n",
    "    return processed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "bAY6iVkJKnei"
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>sentence</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Sehr geehrte Frau Präsidentin!</td>\n",
       "      <td>2023-03-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Liebe Kolleginnen und Kollegen!</td>\n",
       "      <td>2023-03-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Heute vor einem Jahr, am siebten Tag des russi...</td>\n",
       "      <td>2023-03-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>In solchen Minuten überfällt mich die Angst, u...</td>\n",
       "      <td>2023-03-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>In meiner Fantasie spielen sich jetzt schon hu...</td>\n",
       "      <td>2023-03-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>Denn wir sind gut durch diesen Winter gekommen...</td>\n",
       "      <td>2023-03-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>Nichts davon ist eingetreten, weil wir entschl...</td>\n",
       "      <td>2023-03-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Und hinter diesem „Wir“ steht unser ganzes Lan...</td>\n",
       "      <td>2023-03-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>Dadurch sind die Großhandelspreise gefallen; d...</td>\n",
       "      <td>2023-03-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>Wir haben die Gasspeicher so gefüllt, dass sie...</td>\n",
       "      <td>2023-03-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
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       "                                             sentence        date\n",
       "0                      Sehr geehrte Frau Präsidentin!  2023-03-02\n",
       "1                     Liebe Kolleginnen und Kollegen!  2023-03-02\n",
       "2   Heute vor einem Jahr, am siebten Tag des russi...  2023-03-02\n",
       "3   In solchen Minuten überfällt mich die Angst, u...  2023-03-02\n",
       "4   In meiner Fantasie spielen sich jetzt schon hu...  2023-03-02\n",
       "..                                                ...         ...\n",
       "95  Denn wir sind gut durch diesen Winter gekommen...  2023-03-02\n",
       "96  Nichts davon ist eingetreten, weil wir entschl...  2023-03-02\n",
       "97  Und hinter diesem „Wir“ steht unser ganzes Lan...  2023-03-02\n",
       "98  Dadurch sind die Großhandelspreise gefallen; d...  2023-03-02\n",
       "99  Wir haben die Gasspeicher so gefüllt, dass sie...  2023-03-02\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_df = pd.read_csv(\"./example_data.csv\")\n",
    "input_df"
   ]
  },
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at microsoft/mdeberta-v3-base were not used when initializing DebertaV2Model: ['lm_predictions.lm_head.LayerNorm.bias', 'lm_predictions.lm_head.LayerNorm.weight', 'lm_predictions.lm_head.dense.bias', 'mask_predictions.dense.weight', 'mask_predictions.classifier.weight', 'mask_predictions.classifier.bias', 'lm_predictions.lm_head.dense.weight', 'lm_predictions.lm_head.bias', 'mask_predictions.dense.bias', 'mask_predictions.LayerNorm.weight', 'mask_predictions.LayerNorm.bias', 'deberta.embeddings.word_embeddings._weight']\n",
      "- This IS expected if you are initializing DebertaV2Model 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 DebertaV2Model from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
      "/Users/au486628/opt/anaconda3/lib/python3.8/site-packages/transformers/convert_slow_tokenizer.py:446: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.\n",
      "  warnings.warn(\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
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      "text/plain": [
       "  0%|          | 0/13 [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "result = process_dataframe_with_transformer(input_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 826
    },
    "id": "xVcaylRoK5rl",
    "outputId": "31b43655-9956-4e61-b919-9142c8be6c06"
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   "outputs": [
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       "      <td>Denn wir sind gut durch diesen Winter gekommen...</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>Und hinter diesem „Wir“ steht unser ganzes Lan...</td>\n",
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       "    <tr>\n",
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       "      <td>Dadurch sind die Großhandelspreise gefallen; d...</td>\n",
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       "</table>\n",
       "<p>100 rows × 6 columns</p>\n",
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       "                                             sentence        date  \\\n",
       "0                      Sehr geehrte Frau Präsidentin!  2023-03-02   \n",
       "1                     Liebe Kolleginnen und Kollegen!  2023-03-02   \n",
       "2   Heute vor einem Jahr, am siebten Tag des russi...  2023-03-02   \n",
       "3   In solchen Minuten überfällt mich die Angst, u...  2023-03-02   \n",
       "4   In meiner Fantasie spielen sich jetzt schon hu...  2023-03-02   \n",
       "..                                                ...         ...   \n",
       "95  Denn wir sind gut durch diesen Winter gekommen...  2023-03-02   \n",
       "96  Nichts davon ist eingetreten, weil wir entschl...  2023-03-02   \n",
       "97  Und hinter diesem „Wir“ steht unser ganzes Lan...  2023-03-02   \n",
       "98  Dadurch sind die Großhandelspreise gefallen; d...  2023-03-02   \n",
       "99  Wir haben die Gasspeicher so gefüllt, dass sie...  2023-03-02   \n",
       "\n",
       "    morality_binary  emotion_binary  positive_binary  negative_binary  \n",
       "0                 0               0                0                0  \n",
       "1                 0               1                0                0  \n",
       "2                 0               0                0                0  \n",
       "3                 1               1                0                0  \n",
       "4                 1               1                0                0  \n",
       "..              ...             ...              ...              ...  \n",
       "95                0               1                0                0  \n",
       "96                0               0                1                0  \n",
       "97                0               0                0                0  \n",
       "98                0               0                0                0  \n",
       "99                0               0                0                0  \n",
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