skill_classifier/03_transformer_model_bert.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "eef4545a",
   "metadata": {
    "id": "eef4545a"
   },
   "source": [
    "## Let's build a Transformers Classifier using Bert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "AtC6WWNZ8POc",
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     ]
    }
   ],
   "source": [
    "!pip install transformers\n",
    "!pip install evaluate \n",
    "!pip install datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0b769e87",
   "metadata": {
    "id": "0b769e87"
   },
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer\n",
    "from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer, AutoConfig\n",
    "from transformers import DataCollatorWithPadding\n",
    "import evaluate\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "from datasets import Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "372b6bae",
   "metadata": {
    "id": "372b6bae"
   },
   "outputs": [],
   "source": [
    "weights = \"bert-base-german-cased\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ltyENV0O8hiF",
   "metadata": {
    "id": "ltyENV0O8hiF"
   },
   "outputs": [],
   "source": [
    "!mkdir -p data/processed/raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4309e86c",
   "metadata": {
    "id": "4309e86c"
   },
   "outputs": [],
   "source": [
    "# Read training and dev datasets splits\n",
    "BASE_PATH = os.path.join(\"./data/processed/raw\")\n",
    "\n",
    "def get_split(split, base_path=BASE_PATH): \n",
    "    return pd.read_csv(os.path.join(base_path, f\"{split}.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e8cebde3",
   "metadata": {
    "id": "e8cebde3"
   },
   "outputs": [],
   "source": [
    "idx_to_label = {0: \"tech\", 1: \"soft\", 2: \"none\"}\n",
    "label_to_idx = {\"tech\": 0, \"soft\": 1, \"none\": 2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "a2534fd5",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "id": "a2534fd5",
    "outputId": "1aa3ca70-d7df-4c56-ce13-d38dccce2934"
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "        }\n",
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       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                                                text label_id  label\n",
       "0  Wenn Ihnen eine angenehme Arbeitsatmosphäre mi...     none      2\n",
       "1                     ***Abitur / Fachhochschulreife     none      2\n",
       "2      Routinierter Umgang mit MS Office-Anwendungen     tech      0\n",
       "3  Standort univativ GmbH Lavesstr. 80, 30159 Han...     none      2\n",
       "4  Führerschein der Klasse B, wünschenswert BE, C...     soft      1"
      ]
     },
     "execution_count": 103,
     "metadata": {},
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   "source": [
    "train_df = get_split(split=\"training\")[['text', 'label']]\n",
    "train_df.rename({'label':'label_id'}, inplace=True, axis=1)\n",
    "train_df['label'] = train_df['label_id'].apply(lambda x: label_to_idx[x])\n",
    "train_df.head()"
   ]
  },
  {
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   "id": "6d7fd937",
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       "                                                text label_id  label\n",
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    "dev_df = get_split(split=\"dev\")[['text', 'label']]\n",
    "dev_df.rename({'label':'label_id'}, inplace=True, axis=1)\n",
    "dev_df['label'] = dev_df['label_id'].apply(lambda x: label_to_idx[x])\n",
    "dev_df.head()"
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   "id": "T8UrfuSg9eRT",
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       "                                                     [key], {});\n",
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       "\n",
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       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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      ],
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       "                                                text label_id  label\n",
       "0  Umfangreiche Kenntnisse mind. eines der CAD-Sy...     tech      0\n",
       "1                                       Anschreiben:     none      2\n",
       "2       Sehr gute Kenntnisse der Office-Anwendungen.     tech      0\n",
       "3  Sie verfügen über ein hohes Maß an Leistungsfä...     soft      1\n",
       "4  Wenn Sie für Ihre effiziente und umsichtige Ar...     none      2"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = get_split(split=\"test\")[['text', 'label']]\n",
    "test_df.rename({'label':'label_id'}, inplace=True, axis=1)\n",
    "test_df['label'] = test_df['label_id'].apply(lambda x: label_to_idx[x])\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "a3677d00",
   "metadata": {
    "id": "a3677d00"
   },
   "outputs": [],
   "source": [
    "train_data = Dataset.from_pandas(train_df)\n",
    "dev_data = Dataset.from_pandas(dev_df)\n",
    "test_data = Dataset.from_pandas(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "5f69db40",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5f69db40",
    "outputId": "9d7b8591-b47a-4b3f-fa04-bdd42dd5e2b6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'text': 'Wenn Ihnen eine angenehme Arbeitsatmosphäre mit viel Enthusiasmus viel Wert ist, freuen wir uns auf Ihre Bewerbung per E-Mail an recruiting@valyue.de und das persönliche Gespräch mit Ihnen. Frau Asimina Kafida beantwortet Ihre Fragen gerne vorab unter Telefon +49.711.627676-13. Valyue Consulting GmbH www.valyue.de/karriere',\n",
       " 'label_id': 'none',\n",
       " 'label': 2}"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "b75d2727",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b75d2727",
    "outputId": "d7425b9d-f6fb-4714-d45e-5cc2e58d0008"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--bert-base-german-cased/snapshots/702774c02b32a4f360d5fea60ab034d64bf0141c/config.json\n",
      "Model config BertConfig {\n",
      "  \"_name_or_path\": \"bert-base-german-cased\",\n",
      "  \"architectures\": [\n",
      "    \"BertForMaskedLM\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"classifier_dropout\": null,\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\": 30000\n",
      "}\n",
      "\n",
      "loading file vocab.txt from cache at /root/.cache/huggingface/hub/models--bert-base-german-cased/snapshots/702774c02b32a4f360d5fea60ab034d64bf0141c/vocab.txt\n",
      "loading file tokenizer.json from cache at /root/.cache/huggingface/hub/models--bert-base-german-cased/snapshots/702774c02b32a4f360d5fea60ab034d64bf0141c/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-german-cased/snapshots/702774c02b32a4f360d5fea60ab034d64bf0141c/tokenizer_config.json\n",
      "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--bert-base-german-cased/snapshots/702774c02b32a4f360d5fea60ab034d64bf0141c/config.json\n",
      "Model config BertConfig {\n",
      "  \"_name_or_path\": \"bert-base-german-cased\",\n",
      "  \"architectures\": [\n",
      "    \"BertForMaskedLM\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"classifier_dropout\": null,\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\": 30000\n",
      "}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "8f6fec67",
   "metadata": {
    "id": "8f6fec67"
   },
   "outputs": [],
   "source": [
    "def preprocess_function(examples):\n",
    "    return tokenizer(examples[\"text\"], truncation=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "3b0f3865",
   "metadata": {
    "id": "3b0f3865"
   },
   "outputs": [],
   "source": [
    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "6c0b8d17",
   "metadata": {
    "id": "6c0b8d17"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "c56c9b34",
   "metadata": {
    "id": "c56c9b34"
   },
   "outputs": [],
   "source": [
    "def compute_metrics(eval_pred):\n",
    "    predictions, labels = eval_pred\n",
    "    predictions = np.argmax(predictions, axis=1)\n",
    "    f1_score_ = f1_score(labels, predictions, average=\"weighted\")\n",
    "    acc_ = accuracy_score(labels, predictions)\n",
    "    precision_score_ = precision_score(labels,predictions, average=\"weighted\")\n",
    "    recall_score_ = recall_score(labels, predictions, average=\"weighted\")\n",
    "    return {\"accuracy\": acc_, \"f1\": f1_score_, \"prec\": precision_score_, \"recall\": recall_score_}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "318dec06",
   "metadata": {},
   "source": [
    "## Load the pre-trained Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "af69b2e9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "af69b2e9",
    "outputId": "0d6f3bbe-8e88-4b52-e104-3b71d74379c9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--bert-base-german-cased/snapshots/702774c02b32a4f360d5fea60ab034d64bf0141c/config.json\n",
      "Model config BertConfig {\n",
      "  \"_name_or_path\": \"bert-base-german-cased\",\n",
      "  \"architectures\": [\n",
      "    \"BertForMaskedLM\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"tech\",\n",
      "    \"1\": \"soft\",\n",
      "    \"2\": \"none\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"label2id\": {\n",
      "    \"none\": 2,\n",
      "    \"soft\": 1,\n",
      "    \"tech\": 0\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",
      "  \"transformers_version\": \"4.25.1\",\n",
      "  \"type_vocab_size\": 2,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 30000\n",
      "}\n",
      "\n",
      "loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--bert-base-german-cased/snapshots/702774c02b32a4f360d5fea60ab034d64bf0141c/pytorch_model.bin\n",
      "Some weights of the model checkpoint at bert-base-german-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias']\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-german-cased 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"
     ]
    }
   ],
   "source": [
    "model = AutoModelForSequenceClassification.from_pretrained(weights, \n",
    "                                                           num_labels=3, \n",
    "                                                           id2label=idx_to_label, \n",
    "                                                           label2id=label_to_idx)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cce2d04a",
   "metadata": {},
   "source": [
    "## Tokenized the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "117daee1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 113,
     "referenced_widgets": [
      "1d1cc9e7f1f44fb9b0f51c07b60b2307",
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      "66a5dea7cadf4ec48c3977861eb316b5",
      "e9dcd37f70fd4c71be9844c6b836a42f",
      "5d73bedb9a0047a19a02e1a35ca7b152",
      "4125452635dc46a9a633ada8897f657a",
      "8985d1812de443d09048cada79786388",
      "50f3ab7717f94d12a194fed54540c3be",
      "055e4c20d7584aa395bca5dcd2b7e4ff",
      "6c36a9ac39a9436bb94cabe3bcbdcac5",
      "6026c87dec72494a9e734aab50bce486",
      "74f91c899f224f3da64cb2753fdc19ce",
      "63d9e9fbb28448a0b797e9c8a4fae91f",
      "bbd2b7da387d4486a2cda6c2165da7df",
      "9e7c89e05d1848f1a33b8fda5893ad2a",
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      "4a94277be2ab441985e25d9de822690e",
      "deeff09246cd42a18710e3be0ad0cf6d",
      "852090ea51bf4e36acb8bdfb942fc523"
     ]
    },
    "id": "117daee1",
    "outputId": "09cc15c7-304e-439a-eb15-4215c84d3c75"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1d1cc9e7f1f44fb9b0f51c07b60b2307",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/7 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5d73bedb9a0047a19a02e1a35ca7b152",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "500c2773f0ec485da9e038b29bb2d8e9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_tokenized = train_data.map(preprocess_function, batched=True)\n",
    "dev_tokenized = dev_data.map(preprocess_function, batched=True)\n",
    "test_tokenized = test_data.map(preprocess_function, batched=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12db4444",
   "metadata": {},
   "source": [
    "## Finetuning the model with our data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "d1f43393",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d1f43393",
    "outputId": "37a846d3-e330-478f-fb8d-59a5a5ee1b5b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PyTorch: setting up devices\n",
      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
     ]
    }
   ],
   "source": [
    "training_args = TrainingArguments(\n",
    "    output_dir=\"skills_model\",\n",
    "    learning_rate=2e-5,\n",
    "    per_device_train_batch_size=16,\n",
    "    per_device_eval_batch_size=16,\n",
    "    num_train_epochs=4,\n",
    "    weight_decay=0.01,\n",
    "    evaluation_strategy=\"epoch\",\n",
    "    save_strategy=\"epoch\",\n",
    "    load_best_model_at_end=True\n",
    ")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "af421a07",
   "metadata": {
    "id": "af421a07"
   },
   "outputs": [],
   "source": [
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=train_tokenized,\n",
    "    eval_dataset=dev_tokenized,\n",
    "    tokenizer=tokenizer,\n",
    "    data_collator=DataCollatorWithPadding(tokenizer=tokenizer),\n",
    "    compute_metrics=compute_metrics,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "b797b1c0",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "b797b1c0",
    "outputId": "2c585a6d-82a3-45f3-bc19-acece83b194d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following columns in the training set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: label_id, text. If label_id, text 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 = 6975\n",
      "  Num Epochs = 4\n",
      "  Instantaneous batch size per device = 16\n",
      "  Total train batch size (w. parallel, distributed & accumulation) = 16\n",
      "  Gradient Accumulation steps = 1\n",
      "  Total optimization steps = 1744\n",
      "  Number of trainable parameters = 109083651\n",
      "You're using a BertTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1744' max='1744' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1744/1744 02:49, Epoch 4/4]\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>Accuracy</th>\n",
       "      <th>F1</th>\n",
       "      <th>Prec</th>\n",
       "      <th>Recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.165115</td>\n",
       "      <td>0.951044</td>\n",
       "      <td>0.951104</td>\n",
       "      <td>0.951347</td>\n",
       "      <td>0.951044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0.184400</td>\n",
       "      <td>0.177572</td>\n",
       "      <td>0.960836</td>\n",
       "      <td>0.960651</td>\n",
       "      <td>0.960760</td>\n",
       "      <td>0.960836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>0.065700</td>\n",
       "      <td>0.205141</td>\n",
       "      <td>0.957572</td>\n",
       "      <td>0.957345</td>\n",
       "      <td>0.957616</td>\n",
       "      <td>0.957572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0.038400</td>\n",
       "      <td>0.217705</td>\n",
       "      <td>0.959530</td>\n",
       "      <td>0.959350</td>\n",
       "      <td>0.959423</td>\n",
       "      <td>0.959530</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: label_id, text. If label_id, text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1532\n",
      "  Batch size = 16\n",
      "Saving model checkpoint to skills_model/checkpoint-436\n",
      "Configuration saved in skills_model/checkpoint-436/config.json\n",
      "Model weights saved in skills_model/checkpoint-436/pytorch_model.bin\n",
      "tokenizer config file saved in skills_model/checkpoint-436/tokenizer_config.json\n",
      "Special tokens file saved in skills_model/checkpoint-436/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: label_id, text. If label_id, text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1532\n",
      "  Batch size = 16\n",
      "Saving model checkpoint to skills_model/checkpoint-872\n",
      "Configuration saved in skills_model/checkpoint-872/config.json\n",
      "Model weights saved in skills_model/checkpoint-872/pytorch_model.bin\n",
      "tokenizer config file saved in skills_model/checkpoint-872/tokenizer_config.json\n",
      "Special tokens file saved in skills_model/checkpoint-872/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: label_id, text. If label_id, text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1532\n",
      "  Batch size = 16\n",
      "Saving model checkpoint to skills_model/checkpoint-1308\n",
      "Configuration saved in skills_model/checkpoint-1308/config.json\n",
      "Model weights saved in skills_model/checkpoint-1308/pytorch_model.bin\n",
      "tokenizer config file saved in skills_model/checkpoint-1308/tokenizer_config.json\n",
      "Special tokens file saved in skills_model/checkpoint-1308/special_tokens_map.json\n",
      "The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: label_id, text. If label_id, text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Evaluation *****\n",
      "  Num examples = 1532\n",
      "  Batch size = 16\n",
      "Saving model checkpoint to skills_model/checkpoint-1744\n",
      "Configuration saved in skills_model/checkpoint-1744/config.json\n",
      "Model weights saved in skills_model/checkpoint-1744/pytorch_model.bin\n",
      "tokenizer config file saved in skills_model/checkpoint-1744/tokenizer_config.json\n",
      "Special tokens file saved in skills_model/checkpoint-1744/special_tokens_map.json\n",
      "\n",
      "\n",
      "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
      "\n",
      "\n",
      "Loading best model from skills_model/checkpoint-436 (score: 0.16511519253253937).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=1744, training_loss=0.08495615491079628, metrics={'train_runtime': 170.0144, 'train_samples_per_second': 164.104, 'train_steps_per_second': 10.258, 'total_flos': 1146103033567590.0, 'train_loss': 0.08495615491079628, 'epoch': 4.0})"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "614d856e",
   "metadata": {},
   "source": [
    "## Save the best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "5WCqE6iE2x08",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5WCqE6iE2x08",
    "outputId": "07a587c6-4a13-4f69-9383-964493d07c59"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Saving model checkpoint to bert-best-model\n",
      "Configuration saved in bert-best-model/config.json\n",
      "Model weights saved in bert-best-model/pytorch_model.bin\n",
      "tokenizer config file saved in bert-best-model/tokenizer_config.json\n",
      "Special tokens file saved in bert-best-model/special_tokens_map.json\n"
     ]
    }
   ],
   "source": [
    "trainer.save_model(\"bert-best-model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "ohB8B8PkBqp2",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ohB8B8PkBqp2",
    "outputId": "36bb0b08-a58d-46d4-a2f5-409f45b55fb8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bert-best-model/\n",
      "bert-best-model/config.json\n",
      "bert-best-model/vocab.txt\n",
      "bert-best-model/special_tokens_map.json\n",
      "bert-best-model/training_args.bin\n",
      "bert-best-model/tokenizer.json\n",
      "bert-best-model/tokenizer_config.json\n",
      "bert-best-model/pytorch_model.bin\n"
     ]
    }
   ],
   "source": [
    "!tar -czvf best_model_bertv1.tar.gz bert-best-model/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "Qz1gh-i6APDY",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Qz1gh-i6APDY",
    "outputId": "c02ff2c0-6837-49f3-87a9-c8784f327229"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive',force_remount=True)\n",
    "save_to = \"/content/drive/MyDrive/tb_challenge/\"\n",
    "!cp best_model_bertv1.tar.gz $save_to/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71b8201a",
   "metadata": {},
   "source": [
    "## Test Load the Best Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "pn6rwzw24K69",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pn6rwzw24K69",
    "outputId": "9edff118-da2c-4ebf-c996-d0e466ae657f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file ./bert-best-model/config.json\n",
      "Model config BertConfig {\n",
      "  \"_name_or_path\": \"./bert-best-model/\",\n",
      "  \"architectures\": [\n",
      "    \"BertForSequenceClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"tech\",\n",
      "    \"1\": \"soft\",\n",
      "    \"2\": \"none\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"label2id\": {\n",
      "    \"none\": 2,\n",
      "    \"soft\": 1,\n",
      "    \"tech\": 0\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\": \"single_label_classification\",\n",
      "  \"torch_dtype\": \"float32\",\n",
      "  \"transformers_version\": \"4.25.1\",\n",
      "  \"type_vocab_size\": 2,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 30000\n",
      "}\n",
      "\n",
      "loading weights file ./bert-best-model/pytorch_model.bin\n",
      "Some weights of the model checkpoint at ./bert-best-model/ were not used when initializing BertModel: ['classifier.weight', 'classifier.bias']\n",
      "- This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "All the weights of BertModel were initialized from the model checkpoint at ./bert-best-model/.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use BertModel for predictions without further training.\n"
     ]
    }
   ],
   "source": [
    "from transformers.pipelines.base import AutoModel\n",
    "from transformers import BertForSequenceClassification\n",
    "loaded_model = AutoModel.from_pretrained(\"./bert-best-model/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daeb3061",
   "metadata": {},
   "source": [
    "**Create a Classifier Pipeline for evaluating on single text data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "l8eA1kaR5GLG",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "l8eA1kaR5GLG",
    "outputId": "b4c4abab-f392-41fe-de74-127b865a9774"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading file vocab.txt\n",
      "loading file tokenizer.json\n",
      "loading file added_tokens.json\n",
      "loading file special_tokens_map.json\n",
      "loading file tokenizer_config.json\n",
      "loading configuration file ./bert-best-model/config.json\n",
      "Model config BertConfig {\n",
      "  \"_name_or_path\": \"./bert-best-model/\",\n",
      "  \"architectures\": [\n",
      "    \"BertForSequenceClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"tech\",\n",
      "    \"1\": \"soft\",\n",
      "    \"2\": \"none\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"label2id\": {\n",
      "    \"none\": 2,\n",
      "    \"soft\": 1,\n",
      "    \"tech\": 0\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\": \"single_label_classification\",\n",
      "  \"torch_dtype\": \"float32\",\n",
      "  \"transformers_version\": \"4.25.1\",\n",
      "  \"type_vocab_size\": 2,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 30000\n",
      "}\n",
      "\n",
      "loading configuration file ./bert-best-model/config.json\n",
      "Model config BertConfig {\n",
      "  \"_name_or_path\": \"./bert-best-model/\",\n",
      "  \"architectures\": [\n",
      "    \"BertForSequenceClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"tech\",\n",
      "    \"1\": \"soft\",\n",
      "    \"2\": \"none\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"label2id\": {\n",
      "    \"none\": 2,\n",
      "    \"soft\": 1,\n",
      "    \"tech\": 0\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\": \"single_label_classification\",\n",
      "  \"torch_dtype\": \"float32\",\n",
      "  \"transformers_version\": \"4.25.1\",\n",
      "  \"type_vocab_size\": 2,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 30000\n",
      "}\n",
      "\n",
      "loading weights file ./bert-best-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 ./bert-best-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",
      "loading file vocab.txt\n",
      "loading file tokenizer.json\n",
      "loading file added_tokens.json\n",
      "loading file special_tokens_map.json\n",
      "loading file tokenizer_config.json\n"
     ]
    }
   ],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "tokenizer_best = AutoTokenizer.from_pretrained(\"./bert-best-model/\")\n",
    "clf = pipeline(\"text-classification\",\"./bert-best-model/\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "zziYJAEr6aus",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "44f73ac73c994604a53274eaf96300a1",
      "47e3d1a8771d46f6b3981904df988aee",
      "8f916a8160f7454e8da7e805fcff8f49",
      "d67d332bc6aa433abf03b486cd64e234",
      "43243c6b3af946f48b4b743557e885d2",
      "62f8178b294c4eafa9ba5395787f1e92",
      "78c3a537797a4ede8655cca1175968e5",
      "6062aedcc2b34cec97b7b944db7a068b",
      "d344d3e0d1fc41929f518b98a4302141",
      "783452a702664d7c81ccb7ac7b1f3326",
      "b14d50262472448faa87cdb59359b787"
     ]
    },
    "id": "zziYJAEr6aus",
    "outputId": "f52d65ea-b487-44c8-ff01-d81606d3f327"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "44f73ac73c994604a53274eaf96300a1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "text = \"Kenntnisse im Bereich Online-Dienste von Vorteil\"\n",
    "test_tokenized = test_data.map(preprocess_function, batched=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed99fdd5",
   "metadata": {},
   "source": [
    "## Evaluation - Metrics and Reporting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "6aa3e800",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "id": "6aa3e800",
    "outputId": "37b0a094-1b44-435e-8782-91ff3c12c97f"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: label_id, text. If label_id, text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n",
      "***** Running Prediction *****\n",
      "  Num examples = 1502\n",
      "  Batch size = 16\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** Predict ***\n"
     ]
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metrics = {}\n",
    "print(\"*** Predict ***\")\n",
    "results = trainer.predict(test_tokenized)\n",
    "metrics = results.metrics\n",
    "metrics[\"predict_samples\"] = len(test_tokenized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "e280774b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e280774b",
    "outputId": "3ff407fc-530b-4291-bcfc-e4d3c7f0a7cc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'test_loss': 0.1613866090774536, 'test_accuracy': 0.9533954727030626, 'test_f1': 0.9535161205780225, 'test_prec': 0.9543163223140806, 'test_recall': 0.9533954727030626, 'test_runtime': 2.2392, 'test_samples_per_second': 670.773, 'test_steps_per_second': 41.979, 'predict_samples': 1502}\n"
     ]
    }
   ],
   "source": [
    "print(metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "JgDGHOnNPoLD",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JgDGHOnNPoLD",
    "outputId": "f3f7d734-cf03-4db3-a978-0120f0c1f2e1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 0, ..., 0, 1, 2])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_preds = np.argmax(results.predictions, axis=1)\n",
    "y_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "3KnDEou6PzXd",
   "metadata": {
    "id": "3KnDEou6PzXd"
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "G_bFQ99_QO7S",
   "metadata": {
    "id": "G_bFQ99_QO7S"
   },
   "outputs": [],
   "source": [
    "def plot_matrix(y_preds, gold, labels): \n",
    "  c_matrix = confusion_matrix(y_true=gold, y_pred=y_preds, normalize=\"true\")\n",
    "  fig, ax = plt.subplots(figsize=(8,8))\n",
    "  display_matrix = ConfusionMatrixDisplay(confusion_matrix=c_matrix,\n",
    "                                          display_labels=labels)\n",
    "  display_matrix.plot(cmap=\"RdPu\",values_format=\".2f\", ax=ax, colorbar=False)\n",
    "  plt.title(\"Confusion Matrix for BERT model\")\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "iMc8ZHJnQ5vo",
   "metadata": {
    "id": "iMc8ZHJnQ5vo"
   },
   "outputs": [],
   "source": [
    "classes = [\"soft\",\"tech\",\"none\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "M-hIw8DdSSKe",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "M-hIw8DdSSKe",
    "outputId": "2aa4c5a1-d2ed-4a9c-9e79-3e9ae81e221b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gold = np.array(test_data['label'])\n",
    "gold[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "vsDbAbG4RPYA",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 513
    },
    "id": "vsDbAbG4RPYA",
    "outputId": "86d243bb-da26-4fa0-a585-f907d52435bb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_matrix(y_preds, gold, classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9koVE0qnENWX",
   "metadata": {
    "id": "9koVE0qnENWX"
   },
   "source": [
    "## Evaluation on Single text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "nWUoKIPuEM92",
   "metadata": {
    "id": "nWUoKIPuEM92"
   },
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "5MUKPiCBEcXw",
   "metadata": {
    "id": "5MUKPiCBEcXw"
   },
   "outputs": [],
   "source": [
    "best_model = \"skills_model/checkpoint-436\"\n",
    "text = \"Kenntnisse im Bereich Online-Dienste von Vorteil\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "gxYPj9OCEXu3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "gxYPj9OCEXu3",
    "outputId": "a5d852ff-d366-4bbb-e01a-c6d8bf4fb2b7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading file vocab.txt\n",
      "loading file tokenizer.json\n",
      "loading file added_tokens.json\n",
      "loading file special_tokens_map.json\n",
      "loading file tokenizer_config.json\n"
     ]
    }
   ],
   "source": [
    "tokenizer = AutoTokenizer.from_pretrained(best_model)\n",
    "inputs = tokenizer(text, return_tensors=\"pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "VIkXnY6IBU5l",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VIkXnY6IBU5l",
    "outputId": "bb2f4094-8490-484c-ade6-9ebd53a31f7e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file skills_model/checkpoint-436/config.json\n",
      "Model config BertConfig {\n",
      "  \"_name_or_path\": \"bert-base-german-cased\",\n",
      "  \"architectures\": [\n",
      "    \"BertForSequenceClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"tech\",\n",
      "    \"1\": \"soft\",\n",
      "    \"2\": \"none\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"label2id\": {\n",
      "    \"none\": 2,\n",
      "    \"soft\": 1,\n",
      "    \"tech\": 0\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\": \"single_label_classification\",\n",
      "  \"torch_dtype\": \"float32\",\n",
      "  \"transformers_version\": \"4.25.1\",\n",
      "  \"type_vocab_size\": 2,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 30000\n",
      "}\n",
      "\n",
      "loading weights file skills_model/checkpoint-436/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 skills_model/checkpoint-436.\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"
     ]
    }
   ],
   "source": [
    "model = BertForSequenceClassification.from_pretrained(best_model)\n",
    "with torch.no_grad():\n",
    "    logits = model(**inputs).logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "MPHriqCJEt4D",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MPHriqCJEt4D",
    "outputId": "0ca4f4af-9336-4b67-d910-f081bb356fa1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text sample: Kenntnisse im Bereich Online-Dienste von Vorteil \n",
      "Predicted class:tech\n"
     ]
    }
   ],
   "source": [
    "predicted_class_id = logits.argmax().item()\n",
    "print(f\"Text sample: {text} \\nPredicted class:{model.config.id2label[predicted_class_id]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "pDveEbkE__5P",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pDveEbkE__5P",
    "outputId": "05e08a78-6803-4eaa-f498-1b2f664cae3a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': 'tech', 'score': 0.9973288774490356}]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf(text)"
   ]
  }
 ],
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