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/usr/local/lib/python3.9/dist-packages (from datasets) (0.70.14)\n","Requirement already satisfied: packaging in /usr/local/lib/python3.9/dist-packages (from datasets) (23.0)\n","Requirement already satisfied: responses<0.19 in /usr/local/lib/python3.9/dist-packages (from datasets) (0.18.0)\n","Requirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.9/dist-packages (from datasets) (2023.3.0)\n","Requirement already satisfied: aiohttp in /usr/local/lib/python3.9/dist-packages (from datasets) (3.8.4)\n","Requirement already satisfied: pandas in /usr/local/lib/python3.9/dist-packages (from datasets) (1.4.4)\n","Requirement already satisfied: xxhash in /usr/local/lib/python3.9/dist-packages (from datasets) (3.2.0)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from datasets) (1.22.4)\n","Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.9/dist-packages (from datasets) (2.27.1)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/dist-packages (from datasets) (6.0)\n","Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (0.13.2)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from transformers) (3.10.7)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (2022.10.31)\n","Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (6.0.4)\n","Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (22.2.0)\n","Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (1.3.1)\n","Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (1.8.2)\n","Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (1.3.3)\n","Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (2.0.12)\n","Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.9/dist-packages (from aiohttp->datasets) (4.0.2)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub<1.0.0,>=0.11.0->datasets) (4.5.0)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests>=2.19.0->datasets) (2022.12.7)\n","Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests>=2.19.0->datasets) (1.26.15)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests>=2.19.0->datasets) (3.4)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas->datasets) (2022.7.1)\n","Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas->datasets) (2.8.2)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n"]}],"source":["! pip install datasets transformers"]},{"cell_type":"markdown","source":["##Imports"],"metadata":{"id":"PHS16fKu6tVA"}},{"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","import torch\n","from datasets import load_dataset\n","import pickle\n","#from sklearn.model_selection import KFold\n","from sklearn.model_selection import StratifiedKFold\n","import torch\n","import pprint"],"metadata":{"id":"22VUaQR96u0N"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')\n","data_path = \"drive/MyDrive/Mestrado/defesa/data/70-15-15\"\n","model_path = \"drive/MyDrive/Mestrado/defesa/models\""],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sUpfZqq16xIl","executionInfo":{"status":"ok","timestamp":1680281880954,"user_tz":180,"elapsed":2890,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"c0470d4c-2b19-4901-a47c-14e852e9acc0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"code","source":["dataset = load_dataset(\"csv\", data_files={\"train\":data_path+'/comunic_treino.csv', \"test\": data_path+'/comunic_teste.csv', \"validation\":data_path+'/comunic_valid.csv'})"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":86,"referenced_widgets":["cfc08eabdb934ffc80c8baa2fbd99713","6baf46d39ba04fe9be63bd6f2a848b10","732207fda04d4d15bb6845da91a9f0cf","a8e4d4cdcafe4d039c9a58a143f9b629","8bd8ab8af0ea443a8f6a8525043c51b5","233be2ad6bb6488c85595d95d157a741","e44d9ef68e9e402b9a5234e0024c5dd9","0777b6297be14dbb9d2be06a9c9ce6b4","ffaefa3463de44e0b5918d6107d06128","721ddd1684d74bf188c8b0261d1ff672","963138aaa93e40e58de60fd88285c596"]},"id":"FMQJWcN48cLA","executionInfo":{"status":"ok","timestamp":1680281880956,"user_tz":180,"elapsed":16,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"259ea781-1eab-44b3-e943-c32a77fe7774"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:datasets.builder:Found cached dataset csv (/root/.cache/huggingface/datasets/csv/default-72fc5c1f69992a5c/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1)\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cfc08eabdb934ffc80c8baa2fbd99713"}},"metadata":{}}]},{"cell_type":"code","source":["from transformers import BertTokenizer, BertModel\n","\n","tokenizer = BertTokenizer.from_pretrained('neuralmind/bert-base-portuguese-cased')\n","\n","def preprocess_data(docs):\n","    text = docs[\"text\"]\n","    return tokenizer(docs[\"text\"], padding='max_length', max_length=512, truncation=True)"],"metadata":{"id":"zqdSmtZ_8jGJ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["encoded_dataset = dataset.map(preprocess_data, batched=True) #,remove_columns=dataset['train'].column_names)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"70i9ost68nAp","executionInfo":{"status":"ok","timestamp":1680281883239,"user_tz":180,"elapsed":1150,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"6a572815-dee2-491c-9a47-b190e9d68aef"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/csv/default-72fc5c1f69992a5c/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1/cache-b2aca4a97a153375.arrow\n","WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/csv/default-72fc5c1f69992a5c/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1/cache-53f465ae606458ba.arrow\n","WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/csv/default-72fc5c1f69992a5c/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1/cache-f165425398c4d6f2.arrow\n"]}]},{"cell_type":"code","source":["encoded_dataset.set_format(\"torch\")"],"metadata":{"id":"JmuT0xXs8rLD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from transformers import AutoModelForSequenceClassification\n","model = AutoModelForSequenceClassification.from_pretrained(\"neuralmind/bert-base-portuguese-cased\", num_labels=13)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bqZ0eJgp8sDV","executionInfo":{"status":"ok","timestamp":1680281884826,"user_tz":180,"elapsed":1592,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"54bd2a75-7fae-4c32-a7e1-8270a0a8efe9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at neuralmind/bert-base-portuguese-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.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 neuralmind/bert-base-portuguese-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}]},{"cell_type":"code","source":["batch_size = 16\n","metric_name = \"f1\""],"metadata":{"id":"cWl8m9VP9TA4"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from transformers import TrainingArguments, Trainer, EarlyStoppingCallback\n","\n","training_args = TrainingArguments(\n","    f\"bert-finetuned-comunicados\",\n","    evaluation_strategy = \"epoch\",\n","    save_strategy = \"epoch\",\n","    save_total_limit = 5,\n","    learning_rate=1.8e-5,\n","    per_device_train_batch_size=batch_size,\n","    per_device_eval_batch_size=batch_size,\n","    num_train_epochs=10,\n","    weight_decay=0.4,\n","    load_best_model_at_end=True,\n","    metric_for_best_model=metric_name,\n","    push_to_hub=False,\n","    logging_steps=5000,\n","    logging_dir='./logs',\n",")\n"],"metadata":{"id":"wJ8hXWqF9Wba"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from sklearn.metrics import f1_score, roc_auc_score, accuracy_score, precision_score, recall_score, hamming_loss\n","def compute_metrics(p):    \n","    pred, labels = p\n","    pred = np.argmax(pred, axis=1)\n","    accuracy = accuracy_score(y_true=labels, y_pred=pred)\n","    recall = recall_score(y_true=labels, y_pred=pred, average='weighted')\n","    precision = precision_score(y_true=labels, y_pred=pred, average='weighted')\n","    f1 = f1_score(y_true=labels, y_pred=pred, average='weighted')    \n","    return {\"accuracy\": accuracy, \"precision\": precision, \"recall\": recall, \"f1\": f1}    "],"metadata":{"id":"gIy9dBcC9g8U"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from torch import nn\n","from transformers import Trainer\n","\n","# the model is waiting for \"labels\", not \"label\"\n","encoded_dataset = encoded_dataset.rename_column(\"label\", \"labels\")\n","\n","# Get training encoded data into DataFrame\n","df = encoded_dataset[\"train\"].to_pandas()\n","\n","# Get a vector of weights (lowest weight for majority class)\n","class_weights = (1 - (df[\"labels\"].value_counts().sort_index() / len(df))).values\n","print(class_weights)\n","\n","# Put the weights vector to cuda as a Pytorch tensor\n","class_weights = torch.from_numpy(class_weights).float().to(\"cuda\")\n","\n","class CustomTrainer(Trainer):\n","    def compute_loss(self, model, inputs, return_outputs=False):\n","        labels = inputs.get(\"labels\")\n","        # forward pass\n","        outputs = model(**inputs)\n","        logits = outputs.get(\"logits\")\n","        # define loss function with calss weights\n","        loss_func = nn.CrossEntropyLoss(weight=class_weights)\n","        # Compute loss\n","        loss = loss_func(logits, labels)\n","        return (loss, outputs) if return_outputs else loss\n"],"metadata":{"id":"IozFutjpsjYW","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1680281891519,"user_tz":180,"elapsed":3360,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"d23d6b59-2629-41b0-f60e-e3e9a2ac26f8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[0.9372237  0.92838196 0.98496905 0.96905393 0.52961981 0.96374889\n"," 0.98231653 0.94341291 0.98585323 0.90981432 0.97259063 0.98231653\n"," 0.9106985 ]\n"]}]},{"cell_type":"code","source":["trainer = CustomTrainer(\n","    model,\n","    training_args,\n","    train_dataset=encoded_dataset[\"train\"],\n","    eval_dataset=encoded_dataset[\"validation\"],\n","    tokenizer=tokenizer,\n","    compute_metrics=compute_metrics, \n","    callbacks = [EarlyStoppingCallback(early_stopping_patience=3)]\n",")"],"metadata":{"id":"ai7Uhg8CFq_9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["#trainer.train()\n","\n","n_folds = 5\n","kfold = StratifiedKFold(n_splits=n_folds, shuffle=True, random_state=42)\n","\n","# Define a function to perform a single fold of training and evaluation\n","def run_fold(train_dataset, eval_dataset, fold):\n","    print(f\"Training fold {fold}\")\n","    trainer = Trainer(\n","        model=model,                         \n","        args=training_args,                  \n","        train_dataset=train_dataset,         \n","        eval_dataset=eval_dataset,           \n","        compute_metrics=compute_metrics      \n","    )\n","    trainer.train()\n","    print(f\"Evaluating fold {fold}\")\n","    trainer.evaluate()\n"],"metadata":{"id":"G82zNEQALIPB"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Perform k-fold cross-validation\n","\n","history={'accuracy':[],'precision':[],'recall':[],'f1':[]}\n","splits=StratifiedKFold(n_splits=5,shuffle=True,random_state=42)\n","\n","for fold, (train_idx, val_idx) in enumerate(splits.split(np.arange(len(dataset['train'])))):\n","    print('Fold: ',fold+1)\n","    train_sampler=dataset[\"train\"].select(train_idx)\n","    val_sampler=dataset[\"train\"].select(val_idx)\n","    train_dataset = train_sampler.map(preprocess_data, batched=True,remove_columns=dataset['train'].column_names)\n","    train_dataset.set_format(\"torch\")\n","    val_dataset=val_sampler.map(preprocess_data, batched=True,remove_columns=dataset['train'].column_names)\n","    val_dataset.set_format(\"torch\")\n","    model = AutoModelForSequenceClassification.from_pretrained(\"neuralmind/bert-base-portuguese-cased\", num_labels=13)\n","    model.resize_token_embeddings(len(tokenizer))\n","    trainer = CustomTrainer( model,\n","      training_args,\n","      train_dataset=encoded_dataset[\"train\"],\n","      eval_dataset=encoded_dataset[\"validation\"],\n","      tokenizer=tokenizer,\n","      compute_metrics=compute_metrics, \n","      callbacks = [EarlyStoppingCallback(early_stopping_patience=3)]\n","    )\n","    trainer.train()\n","    metrics=trainer.evaluate(eval_dataset=encoded_dataset['test'])\n","    print(\"Métricas dataset teste em \",fold+1,\": \")\n","    pprint.pprint(metrics)\n","    history['precision'].append(metrics['eval_precision'])\n","    history['recall'].append(metrics['eval_recall'])\n","    history['f1'].append(metrics['eval_f1'])\n","    history['accuracy'].append(metrics['eval_accuracy'])\n","\n","    del model\n"],"metadata":{"id":"lWVAhSfIAmaF","colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["67b5b607bc8548a9b71f338da55d6d45","3ba3d2f670f84d8790491d0f90385536","b19a2fb8db5845008a6b0f81ef7f1e24","ba5f094d5897476a843a6ff853ddadd5","43e2e488b80a4e08bb4bfcb5be5b7eba","e45d8525870e47f4ba41a64e71a3ae4c","3c9ab4621c0342cda3738ab4d92ad669","cfec97a12ae24cb380fd0ffa9f3c7ce1","25b6496d35344e0fac825f44385411fd","0d43e9fdb3eb40d4bec7ec8157813f3f","d4766e32057d4e3c9edc53a45fb8d5e2","2fd696120ee94ed78d43284dafdf7bc6","222cec554ca441ec8718fcc5ad7912ab","976ddda772444eda99938ff900ef2c3c","d6d171e3e85e45e484ba96325b41d3e7","242cd62838d74f7da265a9ebcc07c0d6","362a52e84a09466c86e412efa1d80142","491e3b56cad64a97adb582390a94a107","a8e82d41e3224507a0f24db68a4fa30e","09d4db8f675d46b2a2975d18cd5e0841","bea61fed5d9f4988b35dc98108e29401","7156d02eb7d54ca5b9eada756c37fe3c","160b0f0534894c09b9e2b50fbfccec5b","ffd81346d12b4131be58e8c43cab0608","b03df9bdb08b4680a87b1aa257675d26","87884d20ea504a9eb0cd7f2b660945b2","c730f7b35bf3453dade055c9b6e3ff18","079f3def3e264e038964d63518b0298b","65cb87d68a8347e3ac3fd25a8db666e1","6e04c01a5dae4278b7d4229461702f49","2c3f0720fc8c425f93586bc1bfae50cf","8b5869c5bed34b1b9def87fe1938a503","04056ebc40c144afbf1082ee4a3e1315","f6514dcf175742abb584164bcdfadde2","2afb0e3af85b4309bff9a82ae8bb76c5","2c6fb766f8d340a3bf390cd0f20ee9e5","ff9d6fc873a44c0ea20c324369bd48fc","95c1e0c657cb40c68b3ef916d97a0cd1","7c2ce07105624364a3930263d1c17d04","230c4d3c9e364aefa03b4869ca5f87cd","1ae36d35ee2e4f7eb0177f9d3ec4b2ee","4cd5e57ac9cb446ca87ea7ec4708320d","d0c314aa4a67423d98b58da4662fb2a0","a57e2cd2e7f74ac48651cb8b44bc9ae5","17ebc8c555404e19aa2415861167b9b0","4f236335c57545ccad19e0489da85a18","e1c94c0782b94cd3b1cdc663f96533de","a19327fa90c640e9a296351c436f3f1a","17d8162d48d94a4eae65f89fb56eafab","27d38c983e4e40e2af59c27d9d703904","26de7525ecfa40ce8cf1037e5ed9f904","3908758638aa4e2ca5be79fe1b33bf3b","8793096ba59345cba30e083f5baf225b","e519e8344c1343998875d732b3a54fb5","b465c2e0c06f42cf8526a43f04212e4f","46db387fe2fb432bb9d9753807e37ce3","98bf4f1d3ae645fda6cd213083b536fa","c946bacd2de24f4d98835f2bbf88ae8e","a283f26d5ca848839b616d2d3ad88d36","245a8ffddc40454da98b6dcfe30fb024","79a8d29265124b6da434eb5809172381","d8659397bde948aa885e0bd0ccfc264a","ecae4426ef3e4a78a5c0705e40ac1705","deda02d4fcf14ff7a9bc0ee5438f409e","37e4dfbd1755471ab373ec1eef73f3a7","09cdcbfbcc3d4bf485c3ad4c315409bb"]},"executionInfo":{"status":"ok","timestamp":1680287892919,"user_tz":180,"elapsed":4639645,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"9c1fcddb-0362-4692-9a38-85915aeae314"},"execution_count":null,"outputs":[{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["Fold:  1\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/csv/default-72fc5c1f69992a5c/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1/cache-5d7d8f7b1bf9785a.arrow\n","WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/csv/default-72fc5c1f69992a5c/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1/cache-0749df6289441a41.arrow\n","Some weights of the model checkpoint at neuralmind/bert-base-portuguese-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.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 neuralmind/bert-base-portuguese-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:391: 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"]},{"data":{"text/html":["\n","    <div>\n","      \n","      <progress value='710' max='710' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [710/710 19:46, Epoch 10/10]\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>Accuracy</th>\n","      <th>Precision</th>\n","      <th>Recall</th>\n","      <th>F1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>No log</td>\n","      <td>1.428904</td>\n","      <td>0.687243</td>\n","      <td>0.584627</td>\n","      <td>0.687243</td>\n","      <td>0.614796</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>No log</td>\n","      <td>0.997216</td>\n","      <td>0.786008</td>\n","      <td>0.784534</td>\n","      <td>0.786008</td>\n","      <td>0.753790</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>No log</td>\n","      <td>0.841551</td>\n","      <td>0.810700</td>\n","      <td>0.774417</td>\n","      <td>0.810700</td>\n","      <td>0.786671</td>\n","    </tr>\n","    <tr>\n","      <td>4</td>\n","      <td>No log</td>\n","      <td>0.756762</td>\n","      <td>0.831276</td>\n","      <td>0.824762</td>\n","      <td>0.831276</td>\n","      <td>0.820122</td>\n","    </tr>\n","    <tr>\n","      <td>5</td>\n","      <td>No log</td>\n","      <td>0.722548</td>\n","      <td>0.835391</td>\n","      <td>0.835487</td>\n","      <td>0.835391</td>\n","      <td>0.824447</td>\n","    </tr>\n","    <tr>\n","      <td>6</td>\n","      <td>No log</td>\n","      <td>0.732632</td>\n","      <td>0.855967</td>\n","      <td>0.857840</td>\n","      <td>0.855967</td>\n","      <td>0.849446</td>\n","    </tr>\n","    <tr>\n","      <td>7</td>\n","      <td>No log</td>\n","      <td>0.734926</td>\n","      <td>0.864198</td>\n","      <td>0.864940</td>\n","      <td>0.864198</td>\n","      <td>0.858698</td>\n","    </tr>\n","    <tr>\n","      <td>8</td>\n","      <td>No log</td>\n","      <td>0.722990</td>\n","      <td>0.855967</td>\n","      <td>0.861046</td>\n","      <td>0.855967</td>\n","      <td>0.853036</td>\n","    </tr>\n","    <tr>\n","      <td>9</td>\n","      <td>No log</td>\n","      <td>0.725270</td>\n","      <td>0.860082</td>\n","      <td>0.861831</td>\n","      <td>0.860082</td>\n","      <td>0.856062</td>\n","    </tr>\n","    <tr>\n","      <td>10</td>\n","      <td>No log</td>\n","      <td>0.730486</td>\n","      <td>0.855967</td>\n","      <td>0.857994</td>\n","      <td>0.855967</td>\n","      <td>0.850427</td>\n","    </tr>\n","  </tbody>\n","</table><p>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n"]},{"data":{"text/html":["\n","    <div>\n","      \n","      <progress value='16' max='16' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [16/16 00:07]\n","    </div>\n","    "],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["Métricas dataset teste em  1 : \n","{'epoch': 10.0,\n"," 'eval_accuracy': 0.8395061728395061,\n"," 'eval_f1': 0.8253969008143089,\n"," 'eval_loss': 0.7888363003730774,\n"," 'eval_precision': 0.8445719461151561,\n"," 'eval_recall': 0.8395061728395061,\n"," 'eval_runtime': 8.5644,\n"," 'eval_samples_per_second': 28.373,\n"," 'eval_steps_per_second': 1.868}\n","Fold:  2\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/csv/default-72fc5c1f69992a5c/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1/cache-4707dd22efd33734.arrow\n","WARNING:datasets.arrow_dataset:Loading cached processed dataset at /root/.cache/huggingface/datasets/csv/default-72fc5c1f69992a5c/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1/cache-d70a7059a8523e4c.arrow\n","Some weights of the model checkpoint at neuralmind/bert-base-portuguese-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.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 neuralmind/bert-base-portuguese-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:391: 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"]},{"data":{"text/html":["\n","    <div>\n","      \n","      <progress value='98' max='710' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [ 98/710 02:36 < 16:37, 0.61 it/s, Epoch 1.37/10]\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>Accuracy</th>\n","      <th>Precision</th>\n","      <th>Recall</th>\n","      <th>F1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>No log</td>\n","      <td>1.554890</td>\n","      <td>0.625514</td>\n","      <td>0.493714</td>\n","      <td>0.625514</td>\n","      <td>0.536747</td>\n","    </tr>\n","  </tbody>\n","</table><p>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='710' max='710' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [710/710 19:45, Epoch 10/10]\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>Accuracy</th>\n","      <th>Precision</th>\n","      <th>Recall</th>\n","      <th>F1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>No log</td>\n","      <td>1.554890</td>\n","      <td>0.625514</td>\n","      <td>0.493714</td>\n","      <td>0.625514</td>\n","      <td>0.536747</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>No log</td>\n","      <td>1.062443</td>\n","      <td>0.781893</td>\n","      <td>0.727887</td>\n","      <td>0.781893</td>\n","      <td>0.738993</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>No log</td>\n","      <td>0.891670</td>\n","      <td>0.806584</td>\n","      <td>0.785819</td>\n","      <td>0.806584</td>\n","      <td>0.784683</td>\n","    </tr>\n","    <tr>\n","      <td>4</td>\n","      <td>No log</td>\n","      <td>0.790731</td>\n","      <td>0.823045</td>\n","      <td>0.806856</td>\n","      <td>0.823045</td>\n","      <td>0.802789</td>\n","    </tr>\n","    <tr>\n","      <td>5</td>\n","      <td>No log</td>\n","      <td>0.731774</td>\n","      <td>0.818930</td>\n","      <td>0.822704</td>\n","      <td>0.818930</td>\n","      <td>0.814174</td>\n","    </tr>\n","    <tr>\n","      <td>6</td>\n","      <td>No log</td>\n","      <td>0.724035</td>\n","      <td>0.839506</td>\n","      <td>0.830718</td>\n","      <td>0.839506</td>\n","      <td>0.830089</td>\n","    </tr>\n","    <tr>\n","      <td>7</td>\n","      <td>No log</td>\n","      <td>0.700627</td>\n","      <td>0.847737</td>\n","      <td>0.840996</td>\n","      <td>0.847737</td>\n","      <td>0.840618</td>\n","    </tr>\n","    <tr>\n","      <td>8</td>\n","      <td>No log</td>\n","      <td>0.712751</td>\n","      <td>0.847737</td>\n","      <td>0.845079</td>\n","      <td>0.847737</td>\n","      <td>0.841162</td>\n","    </tr>\n","    <tr>\n","      <td>9</td>\n","      <td>No log</td>\n","      <td>0.706047</td>\n","      <td>0.860082</td>\n","      <td>0.858549</td>\n","      <td>0.860082</td>\n","      <td>0.855451</td>\n","    </tr>\n","    <tr>\n","      <td>10</td>\n","      <td>No log</td>\n","      <td>0.711649</td>\n","      <td>0.860082</td>\n","      <td>0.857823</td>\n","      <td>0.860082</td>\n","      <td>0.854644</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='16' max='16' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [16/16 00:07]\n","    </div>\n","    "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Métricas dataset teste em  2 : \n","{'epoch': 10.0,\n"," 'eval_accuracy': 0.8641975308641975,\n"," 'eval_f1': 0.8575576940604206,\n"," 'eval_loss': 0.7591276168823242,\n"," 'eval_precision': 0.862204532913541,\n"," 'eval_recall': 0.8641975308641975,\n"," 'eval_runtime': 8.5129,\n"," 'eval_samples_per_second': 28.545,\n"," 'eval_steps_per_second': 1.88}\n","Fold:  3\n"]},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/905 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"67b5b607bc8548a9b71f338da55d6d45"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/226 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2fd696120ee94ed78d43284dafdf7bc6"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at neuralmind/bert-base-portuguese-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.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 neuralmind/bert-base-portuguese-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:391: 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"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='710' max='710' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [710/710 19:45, Epoch 10/10]\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>Accuracy</th>\n","      <th>Precision</th>\n","      <th>Recall</th>\n","      <th>F1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>No log</td>\n","      <td>1.554890</td>\n","      <td>0.625514</td>\n","      <td>0.493714</td>\n","      <td>0.625514</td>\n","      <td>0.536747</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>No log</td>\n","      <td>1.062443</td>\n","      <td>0.781893</td>\n","      <td>0.727887</td>\n","      <td>0.781893</td>\n","      <td>0.738993</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>No log</td>\n","      <td>0.891670</td>\n","      <td>0.806584</td>\n","      <td>0.785819</td>\n","      <td>0.806584</td>\n","      <td>0.784683</td>\n","    </tr>\n","    <tr>\n","      <td>4</td>\n","      <td>No log</td>\n","      <td>0.790731</td>\n","      <td>0.823045</td>\n","      <td>0.806856</td>\n","      <td>0.823045</td>\n","      <td>0.802789</td>\n","    </tr>\n","    <tr>\n","      <td>5</td>\n","      <td>No log</td>\n","      <td>0.731774</td>\n","      <td>0.818930</td>\n","      <td>0.822704</td>\n","      <td>0.818930</td>\n","      <td>0.814174</td>\n","    </tr>\n","    <tr>\n","      <td>6</td>\n","      <td>No log</td>\n","      <td>0.724035</td>\n","      <td>0.839506</td>\n","      <td>0.830718</td>\n","      <td>0.839506</td>\n","      <td>0.830089</td>\n","    </tr>\n","    <tr>\n","      <td>7</td>\n","      <td>No log</td>\n","      <td>0.700627</td>\n","      <td>0.847737</td>\n","      <td>0.840996</td>\n","      <td>0.847737</td>\n","      <td>0.840618</td>\n","    </tr>\n","    <tr>\n","      <td>8</td>\n","      <td>No log</td>\n","      <td>0.712751</td>\n","      <td>0.847737</td>\n","      <td>0.845079</td>\n","      <td>0.847737</td>\n","      <td>0.841162</td>\n","    </tr>\n","    <tr>\n","      <td>9</td>\n","      <td>No log</td>\n","      <td>0.706047</td>\n","      <td>0.860082</td>\n","      <td>0.858549</td>\n","      <td>0.860082</td>\n","      <td>0.855451</td>\n","    </tr>\n","    <tr>\n","      <td>10</td>\n","      <td>No log</td>\n","      <td>0.711649</td>\n","      <td>0.860082</td>\n","      <td>0.857823</td>\n","      <td>0.860082</td>\n","      <td>0.854644</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='16' max='16' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [16/16 00:07]\n","    </div>\n","    "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Métricas dataset teste em  3 : \n","{'epoch': 10.0,\n"," 'eval_accuracy': 0.8641975308641975,\n"," 'eval_f1': 0.8575576940604206,\n"," 'eval_loss': 0.7591276168823242,\n"," 'eval_precision': 0.862204532913541,\n"," 'eval_recall': 0.8641975308641975,\n"," 'eval_runtime': 8.5025,\n"," 'eval_samples_per_second': 28.58,\n"," 'eval_steps_per_second': 1.882}\n","Fold:  4\n"]},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/905 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"160b0f0534894c09b9e2b50fbfccec5b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/226 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f6514dcf175742abb584164bcdfadde2"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at neuralmind/bert-base-portuguese-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.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 neuralmind/bert-base-portuguese-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:391: 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"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='710' max='710' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [710/710 19:46, Epoch 10/10]\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>Accuracy</th>\n","      <th>Precision</th>\n","      <th>Recall</th>\n","      <th>F1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>No log</td>\n","      <td>1.554890</td>\n","      <td>0.625514</td>\n","      <td>0.493714</td>\n","      <td>0.625514</td>\n","      <td>0.536747</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>No log</td>\n","      <td>1.062443</td>\n","      <td>0.781893</td>\n","      <td>0.727887</td>\n","      <td>0.781893</td>\n","      <td>0.738993</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>No log</td>\n","      <td>0.891670</td>\n","      <td>0.806584</td>\n","      <td>0.785819</td>\n","      <td>0.806584</td>\n","      <td>0.784683</td>\n","    </tr>\n","    <tr>\n","      <td>4</td>\n","      <td>No log</td>\n","      <td>0.790731</td>\n","      <td>0.823045</td>\n","      <td>0.806856</td>\n","      <td>0.823045</td>\n","      <td>0.802789</td>\n","    </tr>\n","    <tr>\n","      <td>5</td>\n","      <td>No log</td>\n","      <td>0.731774</td>\n","      <td>0.818930</td>\n","      <td>0.822704</td>\n","      <td>0.818930</td>\n","      <td>0.814174</td>\n","    </tr>\n","    <tr>\n","      <td>6</td>\n","      <td>No log</td>\n","      <td>0.724035</td>\n","      <td>0.839506</td>\n","      <td>0.830718</td>\n","      <td>0.839506</td>\n","      <td>0.830089</td>\n","    </tr>\n","    <tr>\n","      <td>7</td>\n","      <td>No log</td>\n","      <td>0.700627</td>\n","      <td>0.847737</td>\n","      <td>0.840996</td>\n","      <td>0.847737</td>\n","      <td>0.840618</td>\n","    </tr>\n","    <tr>\n","      <td>8</td>\n","      <td>No log</td>\n","      <td>0.712751</td>\n","      <td>0.847737</td>\n","      <td>0.845079</td>\n","      <td>0.847737</td>\n","      <td>0.841162</td>\n","    </tr>\n","    <tr>\n","      <td>9</td>\n","      <td>No log</td>\n","      <td>0.706047</td>\n","      <td>0.860082</td>\n","      <td>0.858549</td>\n","      <td>0.860082</td>\n","      <td>0.855451</td>\n","    </tr>\n","    <tr>\n","      <td>10</td>\n","      <td>No log</td>\n","      <td>0.711649</td>\n","      <td>0.860082</td>\n","      <td>0.857823</td>\n","      <td>0.860082</td>\n","      <td>0.854644</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='16' max='16' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [16/16 00:07]\n","    </div>\n","    "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Métricas dataset teste em  4 : \n","{'epoch': 10.0,\n"," 'eval_accuracy': 0.8641975308641975,\n"," 'eval_f1': 0.8575576940604206,\n"," 'eval_loss': 0.7591276168823242,\n"," 'eval_precision': 0.862204532913541,\n"," 'eval_recall': 0.8641975308641975,\n"," 'eval_runtime': 8.483,\n"," 'eval_samples_per_second': 28.645,\n"," 'eval_steps_per_second': 1.886}\n","Fold:  5\n"]},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/905 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"17ebc8c555404e19aa2415861167b9b0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/226 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"46db387fe2fb432bb9d9753807e37ce3"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at neuralmind/bert-base-portuguese-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.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 neuralmind/bert-base-portuguese-cased and are newly initialized: ['classifier.bias', 'classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","/usr/local/lib/python3.9/dist-packages/transformers/optimization.py:391: 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"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='710' max='710' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [710/710 19:46, Epoch 10/10]\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>Accuracy</th>\n","      <th>Precision</th>\n","      <th>Recall</th>\n","      <th>F1</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>1</td>\n","      <td>No log</td>\n","      <td>1.554890</td>\n","      <td>0.625514</td>\n","      <td>0.493714</td>\n","      <td>0.625514</td>\n","      <td>0.536747</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>No log</td>\n","      <td>1.062443</td>\n","      <td>0.781893</td>\n","      <td>0.727887</td>\n","      <td>0.781893</td>\n","      <td>0.738993</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>No log</td>\n","      <td>0.891670</td>\n","      <td>0.806584</td>\n","      <td>0.785819</td>\n","      <td>0.806584</td>\n","      <td>0.784683</td>\n","    </tr>\n","    <tr>\n","      <td>4</td>\n","      <td>No log</td>\n","      <td>0.790731</td>\n","      <td>0.823045</td>\n","      <td>0.806856</td>\n","      <td>0.823045</td>\n","      <td>0.802789</td>\n","    </tr>\n","    <tr>\n","      <td>5</td>\n","      <td>No log</td>\n","      <td>0.731774</td>\n","      <td>0.818930</td>\n","      <td>0.822704</td>\n","      <td>0.818930</td>\n","      <td>0.814174</td>\n","    </tr>\n","    <tr>\n","      <td>6</td>\n","      <td>No log</td>\n","      <td>0.724035</td>\n","      <td>0.839506</td>\n","      <td>0.830718</td>\n","      <td>0.839506</td>\n","      <td>0.830089</td>\n","    </tr>\n","    <tr>\n","      <td>7</td>\n","      <td>No log</td>\n","      <td>0.700627</td>\n","      <td>0.847737</td>\n","      <td>0.840996</td>\n","      <td>0.847737</td>\n","      <td>0.840618</td>\n","    </tr>\n","    <tr>\n","      <td>8</td>\n","      <td>No log</td>\n","      <td>0.712751</td>\n","      <td>0.847737</td>\n","      <td>0.845079</td>\n","      <td>0.847737</td>\n","      <td>0.841162</td>\n","    </tr>\n","    <tr>\n","      <td>9</td>\n","      <td>No log</td>\n","      <td>0.706047</td>\n","      <td>0.860082</td>\n","      <td>0.858549</td>\n","      <td>0.860082</td>\n","      <td>0.855451</td>\n","    </tr>\n","    <tr>\n","      <td>10</td>\n","      <td>No log</td>\n","      <td>0.711649</td>\n","      <td>0.860082</td>\n","      <td>0.857823</td>\n","      <td>0.860082</td>\n","      <td>0.854644</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n","/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='16' max='16' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [16/16 00:07]\n","    </div>\n","    "]},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Métricas dataset teste em  5 : \n","{'epoch': 10.0,\n"," 'eval_accuracy': 0.8641975308641975,\n"," 'eval_f1': 0.8575576940604206,\n"," 'eval_loss': 0.7591276168823242,\n"," 'eval_precision': 0.862204532913541,\n"," 'eval_recall': 0.8641975308641975,\n"," 'eval_runtime': 8.5363,\n"," 'eval_samples_per_second': 28.467,\n"," 'eval_steps_per_second': 1.874}\n"]}]},{"cell_type":"code","source":["avg_precision=np.mean(history['precision'])\n","avg_recall=np.mean(history['recall'])\n","avg_f1=np.mean(history['f1'])\n","avg_accuracy=np.mean(history['accuracy'])\n","print(\"***** Resultados Cross Validation *******\")\n","print('Precision:',avg_precision)\n","print('Recall:',avg_recall)\n","print('F1 Score:',avg_f1)\n","print('Accuracy:',avg_accuracy)"],"metadata":{"id":"CdaAeqNrLNS5","executionInfo":{"status":"ok","timestamp":1680288108875,"user_tz":180,"elapsed":339,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"9690d2f0-1a9d-4e85-82e6-9ea6789298a4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["***** Resultados Cross Validation *******\n","Precision: 0.858678015553864\n","Recall: 0.8592592592592592\n","F1 Score: 0.8511255354111983\n","Accuracy: 0.8592592592592592\n"]}]}]}