stance_detection.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hhhuang/NetKu/blob/main/stance_detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gj7w56jPgBQb",
        "outputId": "ec82d9a2-4bfe-433b-8e35-266417707c61"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Counter({'AGAINST': 1395, 'NONE': 766, 'FAVOR': 753})\n",
            "Counter({'Hillary Clinton': 689, 'Feminist Movement': 664, 'Legalization of Abortion': 653, 'Atheism': 513, 'Climate Change is a Real Concern': 395})\n"
          ]
        }
      ],
      "source": [
        "import csv\n",
        "from collections import Counter\n",
        "\n",
        "fin = open(\"/content/drive/MyDrive/Colab Notebooks/stance-detection/StanceDataset/train.csv\")\n",
        "reader = csv.DictReader(fin)\n",
        "labels = Counter()\n",
        "targets = Counter()\n",
        "for line in reader:\n",
        "    labels[line['Stance']] += 1\n",
        "    targets[line['Target']] += 1\n",
        "print(labels)\n",
        "print(targets)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xtvh85DPol5h",
        "outputId": "0140edd3-d565-4d26-f254-9a6e91596cd5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install transformers\n",
        "!pip install datasets\n",
        "!pip install torch\n",
        "!pip install tqdm\n",
        "!pip install evaluate\n",
        "!pip install pandas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "iwrOfm0UjNt1"
      },
      "outputs": [],
      "source": [
        "import csv\n",
        "\n",
        "from collections import defaultdict, Counter\n",
        "\n",
        "import transformers\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModel, AutoConfig\n",
        "from transformers import TrainingArguments, Trainer\n",
        "from transformers import get_scheduler\n",
        "\n",
        "import torch\n",
        "from torch.utils.data import DataLoader\n",
        "from torch.optim import AdamW\n",
        "from torch import nn\n",
        "\n",
        "from datasets import Dataset\n",
        "import evaluate\n",
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "\n",
        "class_weights = torch.tensor([2.0, 1.0, 2.0, 1.0, 1.0, 1.0, 0.5, 0.5]).to(device)\n",
        "main_class_weights = torch.tensor([2.0, 1.0, 2.0, 1.0, 1.0, 1.0, 0.0, 0.0]).to(device)\n",
        "adv_class_weights = torch.tensor([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0]).to(device)\n",
        "\n",
        "loss_fct = torch.nn.BCEWithLogitsLoss(weight=class_weights)\n",
        "main_loss_fct = torch.nn.BCEWithLogitsLoss(weight=main_class_weights)\n",
        "adv_loss_fct = torch.nn.BCEWithLogitsLoss(weight=adv_class_weights)\n",
        "\n",
        "class MultilabelTrainer(Trainer):\n",
        "    def compute_loss(self, model, inputs, return_outputs=False):\n",
        "        labels = inputs.pop(\"labels\")\n",
        "        outputs = model(**inputs)\n",
        "        logits = outputs.logits\n",
        "        loss = loss_fct(logits.view(-1, self.model.config.num_labels), \n",
        "                        labels.float().view(-1, self.model.config.num_labels))\n",
        "        return (loss, outputs) if return_outputs else loss\n",
        "\n",
        "class MultilabelTrainer2(Trainer):\n",
        "    def compute_loss(self, model, inputs, return_outputs=False):\n",
        "        labels = inputs.pop(\"labels\")\n",
        "        outputs = model(**inputs)\n",
        "        logits = outputs.logits\n",
        "        main_logits, main_labels = [], []\n",
        "        adv_logits, adv_labels = [], []\n",
        "        for logit, label in zip(logits, labels):\n",
        "            if label[0] == 0.5:\n",
        "                adv_logits.append(logit)\n",
        "                adv_labels.append(label)\n",
        "            elif label[-1] == 0.5:\n",
        "                main_logits.append(logit)\n",
        "                main_labels.append(label)\n",
        "            else:\n",
        "                raise(\"invalid value: \", label)\n",
        "        loss = torch.tensor(0.0).to(torch.device(\"cuda\"))\n",
        "        if main_labels:\n",
        "            main_logits = torch.cat(main_logits).to(torch.device(\"cuda\")) \n",
        "            main_labels = torch.cat(main_labels).to(torch.device(\"cuda\")) \n",
        "            loss += main_loss_fct(main_logits.view(-1, self.model.config.num_labels), \n",
        "                                  main_labels.float().view(-1, self.model.config.num_labels)) * 0.7\n",
        "        if adv_labels:\n",
        "            adv_logits = torch.cat(adv_logits).to(torch.device(\"cuda\")) \n",
        "            adv_labels = torch.cat(adv_labels).to(torch.device(\"cuda\")) \n",
        "            loss += adv_loss_fct(adv_logits.view(-1, self.model.config.num_labels), \n",
        "                                 adv_labels.float().view(-1, self.model.config.num_labels)) * 0.3             \n",
        "        return (loss, outputs) if return_outputs else loss"
      ],
      "metadata": {
        "id": "RhQwT0eOYBw0"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "wjRuGtiWiIbN"
      },
      "outputs": [],
      "source": [
        "from transformers import TextClassificationPipeline\n",
        "\n",
        "class StanceDetectionModel:\n",
        "    def __init__(self):\n",
        "        self.test_data = \"/content/drive/MyDrive/Colab Notebooks/stance-detection/StanceDataset/test.csv\"\n",
        "        self.training_data = \"/content/drive/MyDrive/Colab Notebooks/stance-detection/StanceDataset/train.csv\"\n",
        "        self.model_path = \"trained_model\"\n",
        "        self.stance_label_mapping = {'AGAINST': 0, 'NONE': 1, 'FAVOR': 2}\n",
        "        self.inverted_stance_label_mapping = {v: k for k, v in self.stance_label_mapping.items()}\n",
        "        self.sentiment_label_mapping = {'neg': 0, 'other': 1, 'pos': 2}\n",
        "        self.inverted_sentiment_label_mapping = {v: k for k, v in self.sentiment_label_mapping.items()}\n",
        "        #topic_label_mapping = {'Hillary Clinton': 0, 'Feminist Movement': 1, 'Legalization of Abortion': 2, 'Atheism': 3, 'Climate Change is a Real Concern': 4, 'Donald Trump': 5}\n",
        "\n",
        "\n",
        "    def prompt_revise(self, topic):\n",
        "        return \"I agree \" + topic\n",
        "\n",
        "    def prompt_revise_negative(self, topic):\n",
        "        return \"I disagree \" + topic\n",
        "\n",
        "    def create_model(self, load_trained=False):\n",
        "        pretrained_model = \"bert-base-uncased\"\n",
        "        self.tokenizer = AutoTokenizer.from_pretrained(pretrained_model)\n",
        "        if load_trained:\n",
        "            self.model = AutoModelForSequenceClassification.from_pretrained(self.model_path)\n",
        "        else:\n",
        "            self.model = AutoModelForSequenceClassification.from_pretrained(pretrained_model, num_labels=8, problem_type=\"multi_label_classification\")\n",
        "        \n",
        "    def load_tweet_data(self, filename, target=None, augmentation=False, adversarial=False):\n",
        "        \"\"\" Data source: https://alt.qcri.org/semeval2016/task6/index.php?id=data-and-tools \"\"\"\n",
        "        import csv\n",
        "        reader = csv.DictReader(open(filename))    # The original released dataset was encoded in ISO-8859-1. The attached version has been changed to utf8 for universality. \n",
        "        cnt = 0\n",
        "        for row in reader:\n",
        "            cnt += 1\n",
        "            if adversarial:\n",
        "                labels = ([0.5] * 6) + ([0.0] * 2)\n",
        "                labels[6 + (cnt % 2)] = 1.0 \n",
        "                yield {'labels': labels, 'text': row['Tweet'], 'topic': self.prompt_revise(row['Target'])}                \n",
        "\n",
        "            if target is not None:\n",
        "                if isinstance(target, str) and row['Target'] != target:\n",
        "                    continue\n",
        "                elif isinstance(target, set) and row['Target'] not in target:\n",
        "                    continue\n",
        "            labels = ([0.0] * 6) + ([0.5] * 2)\n",
        "            labels[self.stance_label_mapping[row['Stance']]] = 1.0\n",
        "            labels[self.sentiment_label_mapping[row['Sentiment']] + 3] = 1.0\n",
        "            yield {'labels': labels, 'text': row['Tweet'], 'topic': self.prompt_revise(row['Target'])}            \n",
        "            if augmentation:\n",
        "                if row['Stance'] == 'AGAINST':\n",
        "                    labels[stance_label_mapping[\"AGAINST\"]] = 0.0\n",
        "                    labels[stance_label_mapping[\"FAVOR\"]] = 1.0\n",
        "                elif row['Stance'] == 'FAVOR':\n",
        "                    labels[stance_label_mapping[\"AGAINST\"]] = 1.0\n",
        "                    labels[stance_label_mapping[\"FAVOR\"]] = 0.0\n",
        "                yield {'labels': labels, 'text': row['Tweet'], 'topic': self.prompt_revise_negative(row['Target'])}\n",
        "            #yield {'label': label_mapping[row['Stance']], 'text': row['Tweet'], 'topic': row['Target']}\n",
        "        \n",
        "    def tokenize_dataset(self, data):\n",
        "        return self.tokenizer(data['topic'], data['text'], return_tensors='pt', truncation=True, padding='max_length', max_length=280)\n",
        "\n",
        "    def compute_metrics(self, eval_pred):\n",
        "        logits, labels = eval_pred\n",
        "        predictions, references = [], []\n",
        "        for i in range(len(logits)):\n",
        "            predictions.append(np.argmax(logits[i][:3]))\n",
        "            references.append(np.argmax(labels[i][:3]))\n",
        "        #predictions = np.argmax(logits, axis=-1)\n",
        "        # Following the evaluation of SemEval-2016, only the F-scores of Favour (2) and Against (0) are measured. \n",
        "        return self.metric.compute(predictions=predictions, references=references, labels=[0, 2], average='macro')\n",
        "\n",
        "    def zeroshot_evaluation(self):\n",
        "        stances = ['Hillary Clinton', 'Feminist Movement', 'Legalization of Abortion', 'Atheism', 'Climate Change is a Real Concern', 'Donald Trump']\n",
        "        for tgt in stances:\n",
        "            self.create_model()\n",
        "            load_training_data = lambda: self.load_tweet_data(self.training_data, set(stances) - set([tgt]), adversarial=True)\n",
        "            load_test_data = lambda: self.load_tweet_data(self.test_data, tgt)\n",
        "            training_data = Dataset.from_generator(load_training_data).shuffle(seed=42).map(self.tokenize_dataset, batched=True)\n",
        "            test_data = Dataset.from_generator(load_test_data).map(self.tokenize_dataset, batched=True)\n",
        "            \n",
        "            training_args = TrainingArguments(\n",
        "                output_dir=\"test_trainer\", \n",
        "                evaluation_strategy=\"epoch\", \n",
        "                num_train_epochs=3,\n",
        "                per_device_train_batch_size=24,\n",
        "                per_device_eval_batch_size=24,\n",
        "            )\n",
        "\n",
        "            self.metric = evaluate.load(\"f1\")\n",
        "            print(tgt)\n",
        "            trainer = MultilabelTrainer(\n",
        "                model=self.model,\n",
        "                args=training_args,\n",
        "                train_dataset=training_data,\n",
        "                eval_dataset=test_data,\n",
        "                compute_metrics=self.compute_metrics,\n",
        "            )\n",
        "            trainer.train()\n",
        "\n",
        "    def train(self):\n",
        "        stances = ['Hillary Clinton', 'Feminist Movement', 'Legalization of Abortion', 'Atheism', 'Climate Change is a Real Concern', 'Donald Trump']\n",
        "        self.create_model()\n",
        "        load_training_data = lambda: self.load_tweet_data(self.training_data, adversarial=True)\n",
        "        load_test_data = lambda: self.load_tweet_data(self.test_data)\n",
        "        training_data = Dataset.from_generator(load_training_data).shuffle(seed=42).map(self.tokenize_dataset, batched=True)\n",
        "        test_data = Dataset.from_generator(load_test_data).map(self.tokenize_dataset, batched=True)\n",
        "            \n",
        "        training_args = TrainingArguments(\n",
        "            output_dir=\"test_trainer\", \n",
        "            evaluation_strategy=\"epoch\", \n",
        "            num_train_epochs=10,\n",
        "            per_device_train_batch_size=24,\n",
        "            per_device_eval_batch_size=24,\n",
        "        )\n",
        "\n",
        "        self.metric = evaluate.load(\"f1\")\n",
        "        trainer = MultilabelTrainer(\n",
        "            model=self.model,\n",
        "            args=training_args,\n",
        "            train_dataset=training_data,\n",
        "            eval_dataset=test_data,\n",
        "            compute_metrics=self.compute_metrics,\n",
        "        )\n",
        "        trainer.train()\n",
        "        trainer.save_model(self.model_path)\n",
        "\n",
        "    def predict(self, instances):\n",
        "        \"\"\" The input is an array of dictinaries. Each dictionary is in the format of \n",
        "            {\n",
        "                \"text\": \"I like Hillary Clinton. We are personal friends. My support for her candidacy is based on the fact that she is brilliantly qualified for the job.\",\n",
        "                \"topic\": \"Hillary Clinton\"\n",
        "            }\n",
        "            The output is an array of predictions for all the input instances. \n",
        "        \"\"\"\n",
        "        pipe = TextClassificationPipeline(model=self.model, tokenizer=self.tokenizer, return_all_scores=True)\n",
        "        predictions = []\n",
        "        for results in pipe([ {\"text\": instance[\"text\"], \"text_pair\": self.prompt_revise(instance[\"topic\"])} for instance in instances]):\n",
        "            predictions.append(max(\n",
        "                [(result['score'], self.inverted_stance_label_mapping[int(result['label'][-1])]) for result in results if result['label'] in ['LABEL_0', 'LABEL_1', 'LABEL_2']])[1])\n",
        "        return predictions"
      ]
    },
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        {
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight']\n",
            "- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
            "WARNING:datasets.builder:Using custom data configuration default-0e70884986d02733\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset generator/default to /root/.cache/huggingface/datasets/generator/default-0e70884986d02733/0.0.0...\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
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        {
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          "text": [
            "Dataset generator downloaded and prepared to /root/.cache/huggingface/datasets/generator/default-0e70884986d02733/0.0.0. Subsequent calls will reuse this data.\n"
          ]
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration default-e33c771de1d7015a\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset generator/default to /root/.cache/huggingface/datasets/generator/default-e33c771de1d7015a/0.0.0...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset generator downloaded and prepared to /root/.cache/huggingface/datasets/generator/default-e33c771de1d7015a/0.0.0. Subsequent calls will reuse this data.\n"
          ]
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d8d67351500445aea3861ad87b532960"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The following columns in the training set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text, topic. If text, topic are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n",
            "***** Running training *****\n",
            "  Num examples = 5828\n",
            "  Num Epochs = 10\n",
            "  Instantaneous batch size per device = 24\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 24\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 2430\n",
            "  Number of trainable parameters = 109488392\n"
          ]
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              "      [ 272/2430 01:30 < 12:01, 2.99 it/s, Epoch 1.12/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>F1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>1</td>\n",
              "      <td>No log</td>\n",
              "      <td>0.605290</td>\n",
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              "  </tbody>\n",
              "</table><p>"
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          "name": "stderr",
          "text": [
            "The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text, topic. If text, topic are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1956\n",
            "  Batch size = 24\n"
          ]
        }
      ],
      "source": [
        "model = StanceDetectionModel()\n",
        "#model.zeroshot_evaluation()\n",
        "model.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "| Topic                     |  F-score |\n",
        "|---------------------------|----------|\n",
        "| Hiliary Clinton           |  0.5373  |\n",
        "| Feminist Movement         |  0.4225  |\n",
        "| Legalization of Abortion  |  0.4685  |\n",
        "| Atheism                   |  0.4488  |\n",
        "| Climate is a Real Concern |  0.2559  |\n",
        "| Supervised                |  0.6223  |"
      ],
      "metadata": {
        "id": "s39ssG_nzmhZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        },
        "id": "9i5bUM62mu-k",
        "outputId": "5ed5e49c-088e-4ee3-b927-08c5b282dcf6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--bert-base-uncased/snapshots/0a6aa9128b6194f4f3c4db429b6cb4891cdb421b/config.json\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"bert-base-uncased\",\n",
            "  \"architectures\": [\n",
            "    \"BertForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.25.1\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n",
            "loading file vocab.txt from cache at /root/.cache/huggingface/hub/models--bert-base-uncased/snapshots/0a6aa9128b6194f4f3c4db429b6cb4891cdb421b/vocab.txt\n",
            "loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--bert-base-uncased/snapshots/0a6aa9128b6194f4f3c4db429b6cb4891cdb421b/tokenizer.json\n",
            "loading file added_tokens.json from cache at None\n",
            "loading file special_tokens_map.json from cache at None\n",
            "loading file tokenizer_config.json from cache at /root/.cache/huggingface/hub/models--bert-base-uncased/snapshots/0a6aa9128b6194f4f3c4db429b6cb4891cdb421b/tokenizer_config.json\n",
            "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--bert-base-uncased/snapshots/0a6aa9128b6194f4f3c4db429b6cb4891cdb421b/config.json\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"bert-base-uncased\",\n",
            "  \"architectures\": [\n",
            "    \"BertForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"transformers_version\": \"4.25.1\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n",
            "loading configuration file trained_model/config.json\n",
            "Model config BertConfig {\n",
            "  \"_name_or_path\": \"trained_model\",\n",
            "  \"architectures\": [\n",
            "    \"BertForSequenceClassification\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"classifier_dropout\": null,\n",
            "  \"gradient_checkpointing\": false,\n",
            "  \"hidden_act\": \"gelu\",\n",
            "  \"hidden_dropout_prob\": 0.1,\n",
            "  \"hidden_size\": 768,\n",
            "  \"id2label\": {\n",
            "    \"0\": \"LABEL_0\",\n",
            "    \"1\": \"LABEL_1\",\n",
            "    \"2\": \"LABEL_2\",\n",
            "    \"3\": \"LABEL_3\",\n",
            "    \"4\": \"LABEL_4\",\n",
            "    \"5\": \"LABEL_5\",\n",
            "    \"6\": \"LABEL_6\",\n",
            "    \"7\": \"LABEL_7\"\n",
            "  },\n",
            "  \"initializer_range\": 0.02,\n",
            "  \"intermediate_size\": 3072,\n",
            "  \"label2id\": {\n",
            "    \"LABEL_0\": 0,\n",
            "    \"LABEL_1\": 1,\n",
            "    \"LABEL_2\": 2,\n",
            "    \"LABEL_3\": 3,\n",
            "    \"LABEL_4\": 4,\n",
            "    \"LABEL_5\": 5,\n",
            "    \"LABEL_6\": 6,\n",
            "    \"LABEL_7\": 7\n",
            "  },\n",
            "  \"layer_norm_eps\": 1e-12,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"position_embedding_type\": \"absolute\",\n",
            "  \"problem_type\": \"multi_label_classification\",\n",
            "  \"torch_dtype\": \"float32\",\n",
            "  \"transformers_version\": \"4.25.1\",\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_cache\": true,\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n",
            "loading weights file trained_model/pytorch_model.bin\n",
            "All model checkpoint weights were used when initializing BertForSequenceClassification.\n",
            "\n",
            "All the weights of BertForSequenceClassification were initialized from the model checkpoint at trained_model.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use BertForSequenceClassification for predictions without further training.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[{'label': 'LABEL_0', 'score': 0.507077693939209}, {'label': 'LABEL_1', 'score': 0.1692151427268982}, {'label': 'LABEL_2', 'score': 0.5552554130554199}, {'label': 'LABEL_3', 'score': 0.33545705676078796}, {'label': 'LABEL_4', 'score': 0.2688095271587372}, {'label': 'LABEL_5', 'score': 0.5860686302185059}, {'label': 'LABEL_6', 'score': 0.49196144938468933}, {'label': 'LABEL_7', 'score': 0.5364270806312561}]\n",
            "[{'label': 'LABEL_0', 'score': 0.3769098222255707}, {'label': 'LABEL_1', 'score': 0.6410229206085205}, {'label': 'LABEL_2', 'score': 0.2685477137565613}, {'label': 'LABEL_3', 'score': 0.673368513584137}, {'label': 'LABEL_4', 'score': 0.3148013651371002}, {'label': 'LABEL_5', 'score': 0.3180817663669586}, {'label': 'LABEL_6', 'score': 0.5091047286987305}, {'label': 'LABEL_7', 'score': 0.4833521842956543}]\n",
            "[{'label': 'LABEL_0', 'score': 0.4163896441459656}, {'label': 'LABEL_1', 'score': 0.1704079806804657}, {'label': 'LABEL_2', 'score': 0.6422132849693298}, {'label': 'LABEL_3', 'score': 0.25878944993019104}, {'label': 'LABEL_4', 'score': 0.23876550793647766}, {'label': 'LABEL_5', 'score': 0.7033905982971191}, {'label': 'LABEL_6', 'score': 0.4771333932876587}, {'label': 'LABEL_7', 'score': 0.5449116826057434}]\n"
          ]
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/transformers/pipelines/text_classification.py:104: UserWarning: `return_all_scores` is now deprecated,  if want a similar funcionality use `top_k=None` instead of `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['FAVOR', 'NONE', 'FAVOR']"
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          "execution_count": 217
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        "model = StanceDetectionModel()\n",
        "model.create_model(True)\n",
        "model.predict([\n",
        "    {\"text\": \"I trust Hillary\", \"topic\": \"Hillary Clinton\"}, \n",
        "    {\"text\": \"I don't like Trump\", \"topic\": \"Hillary Clinton\"},\n",
        "    {\"text\": \"I like Hillary Clinton. We are personal friends. My support for her candidacy is based on the fact that she is brilliantly qualified for the job.\", \"topic\": \"Hillary Clinton\"}\n",
        "    ])"
      ]
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