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BERT com função de perda ponderada (Dataset 70% para treinamento, 15% para teste e 15% para validação)"],"metadata":{"id":"S1s0Ixai6qpO"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xXKVK4HD510J","executionInfo":{"status":"ok","timestamp":1677875867383,"user_tz":180,"elapsed":23825,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"41b64ac9-5f57-45cc-cb2d-27e15afe5885"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting datasets\n","  Downloading datasets-2.10.1-py3-none-any.whl (469 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m469.0/469.0 KB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting transformers\n","  Downloading transformers-4.26.1-py3-none-any.whl (6.3 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python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n","Installing collected packages: tokenizers, xxhash, dill, responses, multiprocess, huggingface-hub, transformers, datasets\n","Successfully installed datasets-2.10.1 dill-0.3.6 huggingface-hub-0.12.1 multiprocess-0.70.14 responses-0.18.0 tokenizers-0.13.2 transformers-4.26.1 xxhash-3.2.0\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"],"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":1677875913170,"user_tz":180,"elapsed":40490,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"55e01397-0a2e-4e15-f4eb-9cae288ee400"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\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":168,"referenced_widgets":["a75b1bd5ecc44e3bb54e5b8272c7f9aa","3ccb05eec2464498a6b0c02dda65d6d3","dd1532796b4842eea33738040b094664","d709e416163b4dad93f20d16293326d1","4fde5e4eb3294e02968c54bb98974c2e","dc50d9626e5e4a3bbe2ea677543c2b13","1ec676abebf74317b7c80eea7f8d0e24","742ac92fe90149f787267b1c3fbef7a6","670a549c9f3440b4a12f7bb8910ff9ab","42378e70cd5b4c3fa3b7dae6cc2183db","d94f943a92ea4b85a90a2031f0415a04","4d3da6209a8b4279a44f50474a1f8130","d6d9b84a9e614fb392b7b8e70439099a","5a61a7d3fb29418b82c1320f37163a1b","0e70faa74ab345aab1ebbd478b4052c4","38854badc53d47458fa510d6d3da3388","0d11e5ce944a404f88a23113c5c05c97","f618bdaebc074c51b85b29321fe3d585","442b8acde6a14eec9f04a3231ca2628d","95336e26d2de46afaa504ebbde33b9df","9f6a7811401b413d90d5dbd51281d621","9aab2a7e30684eb7a39c9a937d3f3a3d","b25f9a0d257740ccba46afee38f3223e","a5af684f9ccf42b9a1268abaad8e7954","a12c2af7b4604f94befd2a82639ab8c4","e122c2fd658d4e30bc380dc9363bbc72","82eee24ea188406d9b6447877aa3a9e1","ec43ef3ff92b4661a3fcef9a7bd6690e","2904dca3d3164cd0811abe242e1fe14d","b086190e945e4c5898c2565a1ee887ca","dfdda4bcbbe84263a9459fce2743a3ed","bc422731947045c79de01e9649ca2851","8250245b5f46469eba3efc6d87b111a3","603d7149fd2b46e7b048532f8b595159","6dc83285769841f285ce6fe83241e5fc","447f348df1b24550b9c91e4650edf6d2","7fa904fdd1f94b289af41c11e17227a3","ea20c8fdf87b482590e5cd077a60b9ac","e71e0df3969945d8888562e237cfe9e4","ed3d0a23cd2d47a690b3aeb2ed88204e","9ab0de433bf54cd2a5d60b1113ea80a1","3f3aaf3291af4d0ba04668da3bf78942","8cbeb3df9f4c49cfb65a09b195f89e59","9d9f0065578246fba82072fd8e020ef1","854c237b45f349d29654745d87eb3022","c5ff763b987544358cf7bc2ad4ee4c5d","0786c01492bb45a0a78757becf871f94","92a2574555d443cfb056356a78073355","f24cc0dc9ac24cddbc3a6409871351c9","8b528878c5b44e98bca6e20f499638a8","824f22be631045d383cba032d25f874e","6debdcf9ddd744889145d3036132ac44","189c4437129c43aea265e9357be7a681","5d99152c55484ab9a5ee139242de886d","c941c79e638348c782f5cb733d6612e8","d4835b6fa7c14e4cbceca81707860ba7","1db41ea65b0b466bb29aebab6534d080","cf3577ca8392498886cb79d89aec3d7e","e0038c67764d4604962c23c533f4b897","086f0b73a23c4eb88d5205b449bfbb6b","ce743420ef6b41e59ef233811c4c9c2f","fe55783557574be89963e4f5dfce5a84","d3d63986e76347f5b9ecff94a1a45184","8f208409f1b24f7e83815b28a87a6110","7772f60a6974484fa1ddaf0dfd90799e","38268f0ef231419d8f8f6d5e107bad2f"]},"id":"FMQJWcN48cLA","executionInfo":{"status":"ok","timestamp":1677875921095,"user_tz":180,"elapsed":7937,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"5ef7da13-cee5-474f-9425-b41aa608a761"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-72fc5c1f69992a5c/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317...\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a75b1bd5ecc44e3bb54e5b8272c7f9aa"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4d3da6209a8b4279a44f50474a1f8130"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b25f9a0d257740ccba46afee38f3223e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"603d7149fd2b46e7b048532f8b595159"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating validation split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"854c237b45f349d29654745d87eb3022"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-72fc5c1f69992a5c/0.0.0/6b34fb8fcf56f7c8ba51dc895bfa2bfbe43546f190a60fcf74bb5e8afdcc2317. Subsequent calls will reuse this data.\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":"d4835b6fa7c14e4cbceca81707860ba7"}},"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","colab":{"base_uri":"https://localhost:8080/","height":177,"referenced_widgets":["5c1bde2ce47d4e1c8e7e46dc9c339a15","79489d76f8dc44c6bf788032ba4bf961","d54b8266acb2406a8ba9f58a72a2a211","6096e7f467b549baa8a2c71ef37fc417","9cd4083bb88442d5add969ec052c34db","7d4033c638e84cbd99d4ded26df6ad8d","b614f3ecf5d940489db0ddd2786e723c","98a455c719f74b58ac3c5f2bba56af4a","f08ebc9099ad48d1be968f65f3d9d382","4cf745e644e146f28ceb6cae268a9b69","8b269827d5174be4a0647af83cc462c9","740c4dc39cbe468ba9ad3e3c4d80d22d","bf27cfde200d4375b72890854c7b38f1","984d4d7e82e04b5bb3ecd9da1915f3b4","13a1b4e9694340e5bde52986a91700ff","484945152ea74c8aa2e4398d01e40d38","f652a3869e124acba07ca8915045b56c","6ce5476d0f154721912f4dc0042c1167","dce0362ae1fa45b7a2817be7c25a2465","769d7dc8c5044ba2838fcec32084d615","53ab32ae41ee4f9ba0fce7b3bacbf5dc","239006d9bbad43e8a30d0d125fb0df9c","bfaf7aa959d84204ae8023a91694a3e3","a38d71fedee7498096833eb5804872fd","76bec8bb51e247a58dfa92607a28cf7b","a346c1bb02224957894748457ae362f2","b66af8d3fb4a40819a69ff76dfe7be58","7a1b283a22a042bc88b9573eb9cb0b80","ec7c8a136a2d4e03b97398bc62facce0","6899a2049fa74c02bc4faf8d5349da09","c8d5f46e2add4427bc8c0a824cc5039e","bdcb42492d5d4523800e6bc2bec97b0b","f4e256f7cd6546628fd62bef2cc8a653","9d867dd16f414b49955d8e8cab83ce14","0e0f2a06334e428e9ce87d221cd4c95d","be494a7e353c4effb4af016eef4cd8b4","e66f36485d154d8cbb74228fee1d028a","c25cb5562e8f4abb815c71d13f50c39c","734db9d5b5b14210a5f438bcbce8e5f8","6e0773688a1949f994ec7362056f4dc7","6f1b5a25161e403787210e104219854c","b45d3514535f49b6b64a54b357536ab0","181b3006970e41879c7f17f364a13ed5","d119ef47ebea4e7f81314569cd754e53","d42f24d7d0d04a1da912cb3be991417f","62c4f49f5d25424f9cb99e562bfd5ff8","03ce10d697d64920843793f21b32a630","b772d977d24c47f6993b5e0dca3b5646","03f34b2ae7c6481c832e05a677f382ec","a529bb84b99247e790405a911170c7ed","f48abd9b2c61471aaee966ae8a09a70e","ba23927cc6884a598acd407f72821349","96593ffb66a14607ace13cf5b14ccb56","af202c101405462e80398e28509417f0","6602543f57cb43a090cd33ad6943d2ad"]},"executionInfo":{"status":"ok","timestamp":1677875933382,"user_tz":180,"elapsed":12304,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"f58b5f05-0c44-4a90-8056-ce82a18e8cac"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/210k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5c1bde2ce47d4e1c8e7e46dc9c339a15"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)in/added_tokens.json:   0%|          | 0.00/2.00 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"740c4dc39cbe468ba9ad3e3c4d80d22d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)cial_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bfaf7aa959d84204ae8023a91694a3e3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)okenizer_config.json:   0%|          | 0.00/43.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9d867dd16f414b49955d8e8cab83ce14"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)lve/main/config.json:   0%|          | 0.00/647 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d42f24d7d0d04a1da912cb3be991417f"}},"metadata":{}}]},{"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/","height":17,"referenced_widgets":["bcff09676b0746359d32fd39d2867c7a","91be507648864cc990acc35614f2ccc8","fb1fa644c11e44549f2be63a929bbb27","31a5b4505ae247c4a7e89a287d081da6","854d857b05c34c5b808c80345cf42a79","4a2bd9bb2f3f45e6922ed591c5973ffe","0f57cc3839a64d098516eaeea43a6967","b53e39f5077e408c9f590246065d504a","0478e16d01c64bec91ab200e1fd6c174","df3aa7a0648a4c4bac78efee9d3a060a","0b93ccda023940b7bd3f2af0083025d5","3171ab94288345f3ba93ef66b0bb2755","ba7fef3bdaa044caa4878ad6ca8058fc","a0c8f52e62ab438693c5eb3e408eb572","fe2d1c51c59049ed9d779bd835fab909","336f5309983d4d5684c80c2457fe65ae","209f55171c5043d29cfa8fad207dc7eb","74e51aa9865f4c46a01d990627fa7f13","880e621f91b440d0a77c48f76e5befc8","91963b0af85147b9bda7e15a736104ef","e2423f8c119a49daa536edbfa0ece359","0a15957057f147468a196fdd3bfe9d6d","601e31f4d7bb4cb69bd27e83a1ab7136","2d62c48b8401481f8bcdce85e02c20c1","a17f03ef66c84424b71f328c2160c58b","da996b415d8a40c69a82d6402e750b30","dd1d6391b042447594acab52900e04bf","9b719e902bba41e2a33f148917d166fe","1c5c7f9747554891abbf987cef7d83b4","dde6e9b8e7914b38883e93cffa177320","4b03ab699f3d4252bf5fd1af5d9b7b38","fb032ee5b0e24218bb096d9eec7dad39","37b25520aa5c49a88639cb18741b44ef"]},"id":"70i9ost68nAp","executionInfo":{"status":"ok","timestamp":1677875944018,"user_tz":180,"elapsed":10653,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"285d8e00-23df-4080-f7fa-835d32c9dced"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/1131 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bcff09676b0746359d32fd39d2867c7a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/243 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3171ab94288345f3ba93ef66b0bb2755"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/243 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"601e31f4d7bb4cb69bd27e83a1ab7136"}},"metadata":{}}]},{"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/","height":156,"referenced_widgets":["3d3dd97718384950a685684e3ade1af3","a3b7ccf1dbf441f89d11dec8e644fc65","ad2a26b40d10459ba28714b1d3183046","d3af7980b71a43018a8a2ed691514820","9f6f5e8afe084399927b80d926f4c8b0","67560e5bfdcc47e7ada378c436e1e5ee","f3ba19c08b5d42dd8b604226a54c9cd6","312bdc0e27b84995a74b7e075aa290e9","6b2f2ad2d427471b8650cb7494194194","e04d3c8f8adb4935ba7e760b34d35665","e4b66feb56a948eeb3668718f679918d"]},"id":"bqZ0eJgp8sDV","executionInfo":{"status":"ok","timestamp":1677875949845,"user_tz":180,"elapsed":5856,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"65b0be32-28b4-4d6c-c5a9-0f035de7c982"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading (…)\"pytorch_model.bin\";:   0%|          | 0.00/438M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3d3dd97718384950a685684e3ade1af3"}},"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.decoder.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', '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 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","args = TrainingArguments(\n","    f\"bert-finetuned-comunicados\",\n","    evaluation_strategy = \"epoch\",\n","    save_strategy = \"epoch\",\n","    save_total_limit = 5,\n","    learning_rate=1.362e-5,\n","    per_device_train_batch_size=batch_size,\n","    per_device_eval_batch_size=batch_size,\n","    num_train_epochs=30,\n","    weight_decay=0.001,\n","    load_best_model_at_end=True,\n","    metric_for_best_model=metric_name,\n","    push_to_hub=False,\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","executionInfo":{"status":"ok","timestamp":1677875957222,"user_tz":180,"elapsed":6260,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"05c621a5-525e-47c7-d8b0-41dc51068362"},"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","    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=4)]\n",")"],"metadata":{"id":"ai7Uhg8CFq_9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["trainer.train()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"G82zNEQALIPB","executionInfo":{"status":"ok","timestamp":1677877168121,"user_tz":180,"elapsed":1210924,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"734c7d65-ef84-4c29-93a9-641e35ca4f81"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["The following columns in the training set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text. If 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 = 1131\n","  Num Epochs = 30\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 = 2130\n","  Number of trainable parameters = 108933133\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='710' max='2130' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [ 710/2130 20:06 < 40:20, 0.59 it/s, Epoch 10/30]\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>1.796253</td>\n","      <td>0.576132</td>\n","      <td>0.398859</td>\n","      <td>0.576132</td>\n","      <td>0.463524</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>No log</td>\n","      <td>1.228515</td>\n","      <td>0.716049</td>\n","      <td>0.589269</td>\n","      <td>0.716049</td>\n","      <td>0.640968</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>No log</td>\n","      <td>0.950041</td>\n","      <td>0.794239</td>\n","      <td>0.750294</td>\n","      <td>0.794239</td>\n","      <td>0.761336</td>\n","    </tr>\n","    <tr>\n","      <td>4</td>\n","      <td>No log</td>\n","      <td>0.800567</td>\n","      <td>0.831276</td>\n","      <td>0.821854</td>\n","      <td>0.831276</td>\n","      <td>0.815984</td>\n","    </tr>\n","    <tr>\n","      <td>5</td>\n","      <td>No log</td>\n","      <td>0.743942</td>\n","      <td>0.843621</td>\n","      <td>0.844078</td>\n","      <td>0.843621</td>\n","      <td>0.836635</td>\n","    </tr>\n","    <tr>\n","      <td>6</td>\n","      <td>No log</td>\n","      <td>0.695417</td>\n","      <td>0.864198</td>\n","      <td>0.862291</td>\n","      <td>0.864198</td>\n","      <td>0.857340</td>\n","    </tr>\n","    <tr>\n","      <td>7</td>\n","      <td>No log</td>\n","      <td>0.769659</td>\n","      <td>0.835391</td>\n","      <td>0.832003</td>\n","      <td>0.835391</td>\n","      <td>0.827737</td>\n","    </tr>\n","    <tr>\n","      <td>8</td>\n","      <td>0.928800</td>\n","      <td>0.739074</td>\n","      <td>0.851852</td>\n","      <td>0.852974</td>\n","      <td>0.851852</td>\n","      <td>0.849343</td>\n","    </tr>\n","    <tr>\n","      <td>9</td>\n","      <td>0.928800</td>\n","      <td>0.753831</td>\n","      <td>0.855967</td>\n","      <td>0.865286</td>\n","      <td>0.855967</td>\n","      <td>0.853606</td>\n","    </tr>\n","    <tr>\n","      <td>10</td>\n","      <td>0.928800</td>\n","      <td>0.808064</td>\n","      <td>0.851852</td>\n","      <td>0.850017</td>\n","      <td>0.851852</td>\n","      <td>0.845411</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n","***** Running Evaluation *****\n","  Num examples = 243\n","  Batch size = 16\n","/usr/local/lib/python3.8/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","Saving model checkpoint to bert-finetuned-comunicados/checkpoint-71\n","Configuration saved in bert-finetuned-comunicados/checkpoint-71/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-71/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-71/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-71/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: text. If text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n","***** Running Evaluation *****\n","  Num examples = 243\n","  Batch size = 16\n","/usr/local/lib/python3.8/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","Saving model checkpoint to bert-finetuned-comunicados/checkpoint-142\n","Configuration saved in bert-finetuned-comunicados/checkpoint-142/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-142/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-142/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-142/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: text. If text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n","***** Running Evaluation *****\n","  Num examples = 243\n","  Batch size = 16\n","/usr/local/lib/python3.8/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","Saving model checkpoint to bert-finetuned-comunicados/checkpoint-213\n","Configuration saved in bert-finetuned-comunicados/checkpoint-213/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-213/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-213/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-213/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: text. If text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n","***** Running Evaluation *****\n","  Num examples = 243\n","  Batch size = 16\n","/usr/local/lib/python3.8/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","Saving model checkpoint to bert-finetuned-comunicados/checkpoint-284\n","Configuration saved in bert-finetuned-comunicados/checkpoint-284/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-284/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-284/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-284/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: text. If text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n","***** Running Evaluation *****\n","  Num examples = 243\n","  Batch size = 16\n","Saving model checkpoint to bert-finetuned-comunicados/checkpoint-355\n","Configuration saved in bert-finetuned-comunicados/checkpoint-355/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-355/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-355/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-355/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: text. If text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n","***** Running Evaluation *****\n","  Num examples = 243\n","  Batch size = 16\n","Saving model checkpoint to bert-finetuned-comunicados/checkpoint-426\n","Configuration saved in bert-finetuned-comunicados/checkpoint-426/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-426/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-426/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-426/special_tokens_map.json\n","Deleting older checkpoint [bert-finetuned-comunicados/checkpoint-71] due to args.save_total_limit\n","The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n","***** Running Evaluation *****\n","  Num examples = 243\n","  Batch size = 16\n","Saving model checkpoint to bert-finetuned-comunicados/checkpoint-497\n","Configuration saved in bert-finetuned-comunicados/checkpoint-497/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-497/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-497/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-497/special_tokens_map.json\n","Deleting older checkpoint [bert-finetuned-comunicados/checkpoint-142] due to args.save_total_limit\n","The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n","***** Running Evaluation *****\n","  Num examples = 243\n","  Batch size = 16\n","Saving model checkpoint to bert-finetuned-comunicados/checkpoint-568\n","Configuration saved in bert-finetuned-comunicados/checkpoint-568/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-568/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-568/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-568/special_tokens_map.json\n","Deleting older checkpoint [bert-finetuned-comunicados/checkpoint-213] due to args.save_total_limit\n","The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n","***** Running Evaluation *****\n","  Num examples = 243\n","  Batch size = 16\n","Saving model checkpoint to bert-finetuned-comunicados/checkpoint-639\n","Configuration saved in bert-finetuned-comunicados/checkpoint-639/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-639/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-639/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-639/special_tokens_map.json\n","Deleting older checkpoint [bert-finetuned-comunicados/checkpoint-284] due to args.save_total_limit\n","The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n","***** Running Evaluation *****\n","  Num examples = 243\n","  Batch size = 16\n","Saving model checkpoint to bert-finetuned-comunicados/checkpoint-710\n","Configuration saved in bert-finetuned-comunicados/checkpoint-710/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-710/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-710/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-710/special_tokens_map.json\n","Deleting older checkpoint [bert-finetuned-comunicados/checkpoint-355] due to args.save_total_limit\n","\n","\n","Training completed. Do not forget to share your model on huggingface.co/models =)\n","\n","\n","Loading best model from bert-finetuned-comunicados/checkpoint-426 (score: 0.8573399717894896).\n"]},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=710, training_loss=0.6874878252056283, metrics={'train_runtime': 1210.5753, 'train_samples_per_second': 28.028, 'train_steps_per_second': 1.759, 'total_flos': 2976079938324480.0, 'train_loss': 0.6874878252056283, 'epoch': 10.0})"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","source":["trainer.evaluate()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":283},"id":"CdaAeqNrLNS5","executionInfo":{"status":"ok","timestamp":1677877176125,"user_tz":180,"elapsed":8039,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"58e8c34a-9245-4d99-e635-9e00a344f656"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["The following columns in the evaluation set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n","***** Running Evaluation *****\n","  Num examples = 243\n","  Batch size = 16\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":"execute_result","data":{"text/plain":["{'eval_loss': 0.6954169869422913,\n"," 'eval_accuracy': 0.8641975308641975,\n"," 'eval_precision': 0.8622908665318176,\n"," 'eval_recall': 0.8641975308641975,\n"," 'eval_f1': 0.8573399717894896,\n"," 'eval_runtime': 8.4017,\n"," 'eval_samples_per_second': 28.923,\n"," 'eval_steps_per_second': 1.904,\n"," 'epoch': 10.0}"]},"metadata":{},"execution_count":15}]},{"cell_type":"markdown","source":["###Avaliação do modelo"],"metadata":{"id":"WbgixlswcWbz"}},{"cell_type":"code","source":["# Define test trainer\n","#test_trainer = Trainer(modelo_carregador) \n","# Make prediction\n","#raw_pred, y_true, _ = test_trainer.predict(encoded_dataset['test']) \n","# Preprocess raw predictions\n","#y_pred = np.argmax(raw_pred, axis=1)"],"metadata":{"id":"8JECLhFYewqE"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["y_pred, y_true, _ = trainer.predict(encoded_dataset['test']) #encoded_dataset['validation']\n","y_pred = np.argmax(y_pred, axis=1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":106},"id":"T_0Bui8UQcdb","executionInfo":{"status":"ok","timestamp":1677877185001,"user_tz":180,"elapsed":8887,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"1355db23-262d-4a4f-81a4-810ef0032609"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["The following columns in the test set don't have a corresponding argument in `BertForSequenceClassification.forward` and have been ignored: text. If text are not expected by `BertForSequenceClassification.forward`,  you can safely ignore this message.\n","***** Running Prediction *****\n","  Num examples = 243\n","  Batch size = 16\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":[]},"metadata":{}}]},{"cell_type":"code","source":["from sklearn import metrics\n","print(metrics.classification_report(y_true,y_pred))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"B0BLAeqoViDM","executionInfo":{"status":"ok","timestamp":1677877185002,"user_tz":180,"elapsed":41,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"5eef31ba-1159-4ffc-851a-821ac9815784"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.88      0.93      0.90        15\n","           1       0.75      0.83      0.79        18\n","           2       0.50      0.33      0.40         3\n","           3       0.86      0.86      0.86         7\n","           4       0.94      0.97      0.96       114\n","           5       0.82      1.00      0.90         9\n","           6       1.00      0.50      0.67         4\n","           7       0.67      0.43      0.52        14\n","           8       0.80      1.00      0.89         4\n","           9       0.72      0.82      0.77        22\n","          10       0.50      0.29      0.36         7\n","          11       1.00      0.75      0.86         4\n","          12       0.86      0.82      0.84        22\n","\n","    accuracy                           0.86       243\n","   macro avg       0.79      0.73      0.75       243\n","weighted avg       0.85      0.86      0.85       243\n","\n"]}]},{"cell_type":"code","source":["%matplotlib inline  \n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","import pandas as pd\n","from sklearn.metrics import classification_report\n","import tensorflow as tf\n","labels = ['AcumCargos', 'ConcAdmis', 'CtrlInt', 'FinPubOrc', 'LicContr', 'Nepotis', 'Obras', 'Outros', 'Previd', 'RemunDirDev', 'ServEduc', 'ServSaude', 'TranspAceInf']\n","\n","def visualize_confusion_matrix(y_pred_argmax, y_true):\n","    cm = tf.math.confusion_matrix(y_pred_argmax, y_true).numpy()\n","    con_mat_df = pd.DataFrame(cm)\n","    #con_mat_df = con_mat_df.drop(columns=con_mat_df.columns[0])\n","    #con_mat_df = con_mat_df.drop(columns=con_mat_df.columns[3])\n","    #con_mat_df = con_mat_df.drop(labels=[0,4], axis=0)\n","    con_mat_df.columns = labels\n","    con_mat_df.set_axis(labels, axis='index', inplace=True)\n","\n","    sns.heatmap(con_mat_df, annot=True, fmt='g', cmap=plt.cm.Blues)\n","    #plt.tight_layout()\n","    plt.title('Confusion Matrix')\n","    plt.ylabel('True label')\n","    plt.xlabel('Predicted label')\n","    plt.show()\n","\n","visualize_confusion_matrix(y_pred, y_true)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":355},"id":"FkZNEKrAaHh3","executionInfo":{"status":"ok","timestamp":1677877186676,"user_tz":180,"elapsed":1711,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"bdad0ed2-d09d-4f89-f4e7-ee1cd5afa81d"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 2 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["trainer.save_model(model_path+\"/BERT_WL_70-15-15.mod\")\n","from transformers import AutoModelForSequenceClassification\n","modelo_carregador = AutoModelForSequenceClassification.from_pretrained(model_path+\"/BERT_WL_70-15-15.mod\")"],"metadata":{"id":"8S0hkaZGeot2","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1677877203544,"user_tz":180,"elapsed":16894,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"5a251863-5c14-42a2-ccc1-d235614daae1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Saving model checkpoint to drive/MyDrive/Mestrado/defesa/models/BERT_WL_70-15-15.mod\n","Configuration saved in drive/MyDrive/Mestrado/defesa/models/BERT_WL_70-15-15.mod/config.json\n","Model weights saved in drive/MyDrive/Mestrado/defesa/models/BERT_WL_70-15-15.mod/pytorch_model.bin\n","tokenizer config file saved in drive/MyDrive/Mestrado/defesa/models/BERT_WL_70-15-15.mod/tokenizer_config.json\n","Special tokens file saved in drive/MyDrive/Mestrado/defesa/models/BERT_WL_70-15-15.mod/special_tokens_map.json\n","loading configuration file drive/MyDrive/Mestrado/defesa/models/BERT_WL_70-15-15.mod/config.json\n","Model config BertConfig {\n","  \"_name_or_path\": \"drive/MyDrive/Mestrado/defesa/models/BERT_WL_70-15-15.mod\",\n","  \"architectures\": [\n","    \"BertForSequenceClassification\"\n","  ],\n","  \"attention_probs_dropout_prob\": 0.1,\n","  \"classifier_dropout\": null,\n","  \"directionality\": \"bidi\",\n","  \"hidden_act\": \"gelu\",\n","  \"hidden_dropout_prob\": 0.1,\n","  \"hidden_size\": 768,\n","  \"id2label\": {\n","    \"0\": \"LABEL_0\",\n","    \"1\": \"LABEL_1\",\n","    \"2\": \"LABEL_2\",\n","    \"3\": \"LABEL_3\",\n","    \"4\": \"LABEL_4\",\n","    \"5\": \"LABEL_5\",\n","    \"6\": \"LABEL_6\",\n","    \"7\": \"LABEL_7\",\n","    \"8\": \"LABEL_8\",\n","    \"9\": \"LABEL_9\",\n","    \"10\": \"LABEL_10\",\n","    \"11\": \"LABEL_11\",\n","    \"12\": \"LABEL_12\"\n","  },\n","  \"initializer_range\": 0.02,\n","  \"intermediate_size\": 3072,\n","  \"label2id\": {\n","    \"LABEL_0\": 0,\n","    \"LABEL_1\": 1,\n","    \"LABEL_10\": 10,\n","    \"LABEL_11\": 11,\n","    \"LABEL_12\": 12,\n","    \"LABEL_2\": 2,\n","    \"LABEL_3\": 3,\n","    \"LABEL_4\": 4,\n","    \"LABEL_5\": 5,\n","    \"LABEL_6\": 6,\n","    \"LABEL_7\": 7,\n","    \"LABEL_8\": 8,\n","    \"LABEL_9\": 9\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","  \"output_past\": true,\n","  \"pad_token_id\": 0,\n","  \"pooler_fc_size\": 768,\n","  \"pooler_num_attention_heads\": 12,\n","  \"pooler_num_fc_layers\": 3,\n","  \"pooler_size_per_head\": 128,\n","  \"pooler_type\": \"first_token_transform\",\n","  \"position_embedding_type\": \"absolute\",\n","  \"problem_type\": \"single_label_classification\",\n","  \"torch_dtype\": \"float32\",\n","  \"transformers_version\": \"4.26.1\",\n","  \"type_vocab_size\": 2,\n","  \"use_cache\": true,\n","  \"vocab_size\": 29794\n","}\n","\n","loading weights file drive/MyDrive/Mestrado/defesa/models/BERT_WL_70-15-15.mod/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 drive/MyDrive/Mestrado/defesa/models/BERT_WL_70-15-15.mod.\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"]}]},{"cell_type":"code","source":["!pip install transformers_interpret"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"x9SZ7u8JTPz2","executionInfo":{"status":"ok","timestamp":1677877217373,"user_tz":180,"elapsed":13839,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"d08835f6-ad91-40e0-eca0-ab2bac2de445"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting transformers_interpret\n","  Downloading transformers_interpret-0.9.6-py3-none-any.whl (45 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.6/45.6 KB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting ipython<8.0.0,>=7.31.1\n","  Downloading ipython-7.34.0-py3-none-any.whl (793 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m793.8/793.8 KB\u001b[0m \u001b[31m59.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hCollecting captum>=0.3.1\n","  Downloading captum-0.6.0-py3-none-any.whl (1.3 MB)\n","\u001b[2K     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urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->transformers>=3.0.0->transformers_interpret) (1.26.14)\n","Installing collected packages: matplotlib-inline, jedi, ipython, captum, transformers_interpret\n","  Attempting uninstall: ipython\n","    Found existing installation: ipython 7.9.0\n","    Uninstalling ipython-7.9.0:\n","      Successfully uninstalled ipython-7.9.0\n","\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n","google-colab 1.0.0 requires ipython~=7.9.0, but you have ipython 7.34.0 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed captum-0.6.0 ipython-7.34.0 jedi-0.18.2 matplotlib-inline-0.1.6 transformers_interpret-0.9.6\n"]}]},{"cell_type":"code","source":["from transformers_interpret import SequenceClassificationExplainer\n","df=pd.DataFrame(dataset['test'])\n","df.iloc[19].text"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":70},"id":"US-FvwyzDDQm","executionInfo":{"status":"ok","timestamp":1677877217375,"user_tz":180,"elapsed":55,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"d1e9556b-f62a-4ce0-e376-354d0559b157"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["'o denunciativo compareceu a esta ouvidoria para comunicar que a esposa do prefeito da cidade de prata tem a posse de um veiculo wv placa piw renavan que é de propriedade do pai do dono da empresa que presta serviço de transporte escolar e locação de um veiculo da marca toiota railux para prefeitura da cidade e que armazena prefeito armazena combustível no almoxarifado da prefeitura para abastecer os carros do transporte escolar no entendimento do denunciante os carros deveriam ser abastecidos pelo empresa contratada'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":22}]},{"cell_type":"code","source":["comunic=pd.read_csv(data_path+'/comunic_teste.csv')\n","transp=comunic[comunic.label==1]\n","cls_explainer = SequenceClassificationExplainer(modelo_carregador, tokenizer)\n","\n","for cada in transp[\"text\"][:3].values:\n","    word_attributions = cls_explainer(cada)\n","    cls_explainer.visualize()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":531},"id":"V7DtI694DQLV","outputId":"0e232f9e-d6d2-4e48-e921-6549c8b6b309","executionInfo":{"status":"ok","timestamp":1677877348169,"user_tz":180,"elapsed":130841,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}}},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["<table width: 100%><div style=\"border-top: 1px solid; 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