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BERT com função de perda ponderada e reamostragem (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":1677877508293,"user_tz":180,"elapsed":32477,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"ddc364a5-89cc-41c7-a231-e16ddbacd292"},"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[31m10.1 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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already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub<1.0.0,>=0.2.0->datasets) (4.5.0)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (2.10)\n","Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (1.26.14)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (2022.12.7)\n","Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (4.0.0)\n","Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2.8.2)\n","Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2022.7.1)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from 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":1677877535464,"user_tz":180,"elapsed":23196,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"820e8f62-6c01-430d-c56e-16ca6365c14a"},"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_aug.csv', \"test\": data_path+'/comunic_teste.csv', \"validation\":data_path+'/comunic_valid.csv'})"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":168,"referenced_widgets":["ab93dff3ee934fe992f6c46cf1551388","1d9a3fbf99634e10a9c85121498935d4","bda7af0e123a4257b5f8cdedb63bf81b","118dab419e54464c8ffb71d217944c86","3c93ef9372fb45c5a23a8951487d9e64","8cd6adea42ba43cf9e40aa228c48bcb8","fc5109edbd2d40e0822484d143e6f563","ff3c012538254b0a93904b92e241152a","2ee904232fce4ce49641e4fee85dad90","afa88a1e440b488783f78b37fff0ecd7","60091eca90224a8a9cabd76044b46586","08cf61995c49450f94f722b71015639c","6d0886475ef74da8ac112359e83b2e5f","6bcb56c9671347d99d2c527c41e46244","151962dcca194884806adcc3ff8a7ac6","f5283462a7c64f4eb5fe1f5037684188","7c478a9c24494d748690dc9693841f69","95b82d2b030d4c61be59dbc3381bff47","0a95afb572d6460b902e8961e3b9be25","534196bee76e43518188a0582fcf8d4b","fbbc70ab067d46d381cbf37b081d6618","96445cae3dc04ae8a5579a6eee5b9dc8","7b591d96614e4a058f65ab250443b9ec","815d12ac9e15445f84b969460f15583a","4a1d64908b5b490699fd80a474f6f27a","1be118983158435e974cfe1d66bd96c7","f17e6054d5994bd087e2f47108410294","8d0306b1c58842f0a693ca58eeaa1056","a55cf287eabf417692f2873fc86e1e58","d1ac67f6d91f49fcb9461a876ea1687f","08df397ea8184d19a0082cca8fb1464f","6efb655d2bdd49328607905579f6125b","b627e277d3eb486e85f393e684ec6c7b","49bc1e86cb5349b989bc3daae12a6a73","0aa0991c2a2b45229eebb9f88546eff5","66b52bc03b5a4accb8f52688b202da7c","32597bb71ed043f28aca62f5d3ca42a8","0ff0baa763bd48d1b15190c2775475b0","410fe759dbb64142acf2b3fd7b9856a2","7251e47f979a48f9b67cbc3679c1b402","21e9e400e27d4ed292e507dfe0af417c","659e781b41d04d11af8555061bf19060","497cf0a06c374537bc1f406ffdf326d7","d79016ca28d647d78dab9b58c401b10d","2cef108f87c84281bb9e69da290d7cd9","9b4a19ceb0624a9cbf8527b6273e1a0b","9a6ca627d0de43f29f5d21f3cd77c6f0","b2d980f4531d454d96fc9038f62af6b8","611e6e2004c948df962828a0b044b7b6","08ddb336ac5941a582e695bc847ebdd6","f6ecbf4b310247bd8639b1921641c72c","b532c5efa11b4c43954d27deba55398f","494a24a470e1473192ad133cbea8dc68","cbc6f687628f49e9824e1ed5238c87fb","bc062933b1794c24ade1a7a2e9e5d631","4d6ff1d4db6f49cb9754408310ecb47c","66a77182f9374c539d4d632767448b55","440d3179c9c04dcd9fee45d41d2995ca","25ac3544709442f098dfb4decd3a264c","1c8f6cbe1d734c5fb6bfa629144b4afd","50be3fbfef5c465f92949cddaf1d5148","96a328bc1576406c80dc28ba4ca9f466","ffa131113f044d649b7ff2c9f5e96551","03d91cd5d50f4f4c942cc13e3c74bdca","311a56d189d14f79afbd69a72866acb8","f5332877ac1c47b09ef2ce9e46a813ae"]},"id":"FMQJWcN48cLA","executionInfo":{"status":"ok","timestamp":1677877538253,"user_tz":180,"elapsed":2837,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"0ab2087b-8a36-4b0c-e75d-c70c299d9fd0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-c30bd544b75348fe/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":"ab93dff3ee934fe992f6c46cf1551388"}},"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":"08cf61995c49450f94f722b71015639c"}},"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":"7b591d96614e4a058f65ab250443b9ec"}},"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":"49bc1e86cb5349b989bc3daae12a6a73"}},"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":"2cef108f87c84281bb9e69da290d7cd9"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/csv/default-c30bd544b75348fe/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":"4d6ff1d4db6f49cb9754408310ecb47c"}},"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":["d504084d00a348a2bd54308277827116","88eac9b5e20a47d18b82453ae5fdfd36","99aa4c8b22424ce2a053cb698bc42c5b","88b5e3d6e4bb4b2aadd543734f0a01e4","9233689d586c444a85b566215fcfc11a","b1eb08c6400f4975bb210f9d767209ea","58f4d5a2c06541a590fff87379c570cf","831d6f818b134df284902f69a17dcc9b","3815d6ace3b6448ab4e59e38c24ab8d8","a4deac3c02934231b7712eb95f81729c","96404fbc4be2463fb36f70aef74b9ef9","16efc37ce60246c5a8efb17c60ef3b4a","e5231d48cd244313b68f5661960ebb55","aa66426fdec544788401cc23d108e414","1e1cbb7f089b44aaae10d038e3623f86","d0986e7e5d4d4703814a81cccbafa944","d49c3cc330124fd395af1f6b17f26bed","09691d7bbb964ad994bd51589e6fba6f","c2a777cf0fee4bbe858c78e8976583e6","0d325a3eb1fd4938904a4cb7cc586cd9","55ed4dca30ef4769829aa06b95848477","69ffc226043a4a6d8032bf4158fc20ec","10c0fb76514e4e1182797c5b76dad0da","6822c90f593d4fdd8eca467c36211c9c","79610d888f4444e2bbe07c14f7228b8c","f520bea7209c488fb4542c1437c50f03","513c76370ae046d4bbd1937147fc6568","68539c271f5046299d2d9a83ad872042","f5a84820885d4cf1bb0d9692f41e242d","fc027d40498445b594227acb1cc15651","d8f01098bea9492b9e4ddb126cf03c3f","07cf0e474c0942cb821ef9718a234da7","408ba8f3cb4d4e2b9d9c6b7078b7870b","ab6e6ebc947d42ec9b2c93a4f00d4d86","86950252a27441ed841aaf62e15bf94e","8fb56fff98d949348427084d8bb693a3","504a3ed2064145e88fe9195b60465dc4","21a2114ac88b48faa3f64b2012804e93","ee63365ffb92424fa583b80b45e52079","5100fc6d0f06400ca19b29ab55690826","ba3829a2ec1841cebf28b0a967ed036e","590f754bccfb4469857bfc91bb06fd18","14dc49b862c84e4fbe2179e93eac2015","bdf78ca3768f44868586258d4185b5a8","1cd9fa4a9b3942fc84ce0a0808fc151c","fda8a9689f96495caa08ff8132534d43","e875919152fb438ab10121781bd03315","c4a655d593e2444989d5abc645ae0977","987c6ad9227743fdbef97d3569470801","d89f8cf7792c4eb0a712dc604334783e","e7ffcfb075e94239b6e77a78c6bb6981","4e73d0091d1f4cac9994dcdff6127bae","c3eefac323ad465cb81ab8394940cc6e","84b9f9e6744b4087a04b3b9044a55ec4","53eae2a0410848829802b968b150ac4c"]},"executionInfo":{"status":"ok","timestamp":1677877549363,"user_tz":180,"elapsed":11120,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"a9204f35-c493-4beb-9b6a-67e273f5e04b"},"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":"d504084d00a348a2bd54308277827116"}},"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":"16efc37ce60246c5a8efb17c60ef3b4a"}},"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":"10c0fb76514e4e1182797c5b76dad0da"}},"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":"ab6e6ebc947d42ec9b2c93a4f00d4d86"}},"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":"1cd9fa4a9b3942fc84ce0a0808fc151c"}},"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":["6140f4f2f739437bb40d22f48793ac86","257a9c355f1d4f809251a2412d12ab84","b53af97d42774695bc57fd88f75a2320","e6eed8192c334cbdb75fb531ccf5d0e3","cd029b3a59fe4f30b3365af60d758a41","086741e7850e497b8f16f5bbb98c625b","233c781d84b54a89844b7c28dafe1e85","958aea758c40452bb9f8ccdd473c3041","55d498446a944474b93aa3d28c5374ca","48a003951e4e406c88e44f85283384d0","472170e6dce4429482784703737f8f22","4bcba50bd63446a5989aec45852cc958","c0b09dccdbe84b709aebf6404bc8cea6","02784c8ab90049c8b13489b7e4030baa","b8fd4574c7214279858a20eb04e41329","e8885a36c08741a49076b8fcf6624286","cd8425fc677f49d59bdad67b22903d5a","7d8111451dc34ba18f80be1a5ccd6565","48948316624947d99b47631199223070","806fae4929ef417ab9598704e1174317","c657e592f64f446cb484335713d1c433","a3634576516247cb95be15fdc3845f07","6d90a280642f4fcab7b959a9b886e646","8da6cd77338c4912b524223b30b975a4","229f449c1c9e42e3a57969b0584d029b","a526323c782341059f6860bb009beca6","7e9ad0c15ea146d9bcd871a126527924","4703b610cf914835918aca6ef3dcca29","32944dafd81946719294990d530680c9","8d48cb2a97d44560bc656994b0af6a3a","71c8d48a42d04951b5e08a5ca5e590bc","c5f3899a67b14fe9955a37831371c495","3b981bdfc2bc4e47bb91044d5d24245a"]},"id":"70i9ost68nAp","executionInfo":{"status":"ok","timestamp":1677877569853,"user_tz":180,"elapsed":20501,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"7d0a9263-09cd-47e5-94c0-2eeb60a7877e"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/2932 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6140f4f2f739437bb40d22f48793ac86"}},"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":"4bcba50bd63446a5989aec45852cc958"}},"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":"6d90a280642f4fcab7b959a9b886e646"}},"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":["753c06a133cd4b8cba3dd1f9f33cf4b7","6b4c6b10a0bd4a0a9eb59db50170acb7","0a85c536b0b34fea9b40e9875ae1d0fa","eb00e26bcb3043809c7ea5161503a9a7","8494fdb9e7504a3b89772f403909e39b","4975ff06b18343f8b54f69bb0bfeca2c","f5cb17f4c58e4c5dacdd736afa95d174","06722b7789294da39774c80b0f12fe82","91eede6a77524765ad7bf8320b687675","d6655e803e4243f78c888b4c8f8e53fb","aeed60d9aa8c4f5da01e736f0be05a18"]},"id":"bqZ0eJgp8sDV","executionInfo":{"status":"ok","timestamp":1677877572947,"user_tz":180,"elapsed":3137,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"3b4829ec-8f03-4208-db17-9cdda16a4b83"},"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":"753c06a133cd4b8cba3dd1f9f33cf4b7"}},"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.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.bias', 'cls.predictions.bias', 'cls.predictions.decoder.weight']\n","- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of BertForSequenceClassification were not initialized from the model checkpoint at neuralmind/bert-base-portuguese-cased and are newly initialized: ['classifier.weight', 'classifier.bias']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}]},{"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","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1677877580889,"user_tz":180,"elapsed":5294,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"d4cd247f-36b5-476e-e7f2-6bf4743d9322"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[0.93178718 0.93178718 0.93178718 0.93178718 0.81855389 0.93178718\n"," 0.93178718 0.93178718 0.93178718 0.93178718 0.93178718 0.93178718\n"," 0.93178718]\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","outputId":"9e724ab1-7eae-45c4-c826-9387498d9951","executionInfo":{"status":"ok","timestamp":1677881838955,"user_tz":180,"elapsed":4258082,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}}},"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 = 2932\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 = 5520\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='2760' max='5520' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [2760/5520 1:10:54 < 1:10:57, 0.65 it/s, Epoch 15/30]\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>0.924138</td>\n","      <td>0.814815</td>\n","      <td>0.795319</td>\n","      <td>0.814815</td>\n","      <td>0.791260</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>No log</td>\n","      <td>0.654362</td>\n","      <td>0.843621</td>\n","      <td>0.853430</td>\n","      <td>0.843621</td>\n","      <td>0.835787</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>0.972100</td>\n","      <td>0.631166</td>\n","      <td>0.851852</td>\n","      <td>0.849096</td>\n","      <td>0.851852</td>\n","      <td>0.844372</td>\n","    </tr>\n","    <tr>\n","      <td>4</td>\n","      <td>0.972100</td>\n","      <td>0.702715</td>\n","      <td>0.855967</td>\n","      <td>0.868617</td>\n","      <td>0.855967</td>\n","      <td>0.857213</td>\n","    </tr>\n","    <tr>\n","      <td>5</td>\n","      <td>0.972100</td>\n","      <td>0.760703</td>\n","      <td>0.868313</td>\n","      <td>0.868846</td>\n","      <td>0.868313</td>\n","      <td>0.862277</td>\n","    </tr>\n","    <tr>\n","      <td>6</td>\n","      <td>0.075000</td>\n","      <td>0.836950</td>\n","      <td>0.860082</td>\n","      <td>0.865936</td>\n","      <td>0.860082</td>\n","      <td>0.856887</td>\n","    </tr>\n","    <tr>\n","      <td>7</td>\n","      <td>0.075000</td>\n","      <td>0.854055</td>\n","      <td>0.864198</td>\n","      <td>0.866487</td>\n","      <td>0.864198</td>\n","      <td>0.859258</td>\n","    </tr>\n","    <tr>\n","      <td>8</td>\n","      <td>0.075000</td>\n","      <td>0.907046</td>\n","      <td>0.868313</td>\n","      <td>0.863740</td>\n","      <td>0.868313</td>\n","      <td>0.859873</td>\n","    </tr>\n","    <tr>\n","      <td>9</td>\n","      <td>0.012800</td>\n","      <td>0.919531</td>\n","      <td>0.868313</td>\n","      <td>0.871070</td>\n","      <td>0.868313</td>\n","      <td>0.863865</td>\n","    </tr>\n","    <tr>\n","      <td>10</td>\n","      <td>0.012800</td>\n","      <td>0.939029</td>\n","      <td>0.872428</td>\n","      <td>0.873810</td>\n","      <td>0.872428</td>\n","      <td>0.867204</td>\n","    </tr>\n","    <tr>\n","      <td>11</td>\n","      <td>0.006000</td>\n","      <td>0.939572</td>\n","      <td>0.880658</td>\n","      <td>0.880405</td>\n","      <td>0.880658</td>\n","      <td>0.874851</td>\n","    </tr>\n","    <tr>\n","      <td>12</td>\n","      <td>0.006000</td>\n","      <td>0.966462</td>\n","      <td>0.868313</td>\n","      <td>0.871070</td>\n","      <td>0.868313</td>\n","      <td>0.863865</td>\n","    </tr>\n","    <tr>\n","      <td>13</td>\n","      <td>0.006000</td>\n","      <td>0.965656</td>\n","      <td>0.868313</td>\n","      <td>0.873574</td>\n","      <td>0.868313</td>\n","      <td>0.865618</td>\n","    </tr>\n","    <tr>\n","      <td>14</td>\n","      <td>0.005200</td>\n","      <td>0.998423</td>\n","      <td>0.868313</td>\n","      <td>0.872744</td>\n","      <td>0.868313</td>\n","      <td>0.864690</td>\n","    </tr>\n","    <tr>\n","      <td>15</td>\n","      <td>0.005200</td>\n","      <td>0.998012</td>\n","      <td>0.868313</td>\n","      <td>0.872744</td>\n","      <td>0.868313</td>\n","      <td>0.864690</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-184\n","Configuration saved in bert-finetuned-comunicados/checkpoint-184/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-184/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-184/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-184/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-368\n","Configuration saved in bert-finetuned-comunicados/checkpoint-368/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-368/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-368/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-368/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-552\n","Configuration saved in bert-finetuned-comunicados/checkpoint-552/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-552/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-552/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-552/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-736\n","Configuration saved in bert-finetuned-comunicados/checkpoint-736/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-736/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-736/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-736/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-920\n","Configuration saved in bert-finetuned-comunicados/checkpoint-920/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-920/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-920/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-920/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-1104\n","Configuration saved in bert-finetuned-comunicados/checkpoint-1104/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-1104/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-1104/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-1104/special_tokens_map.json\n","Deleting older checkpoint [bert-finetuned-comunicados/checkpoint-184] 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-1288\n","Configuration saved in bert-finetuned-comunicados/checkpoint-1288/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-1288/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-1288/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-1288/special_tokens_map.json\n","Deleting older checkpoint [bert-finetuned-comunicados/checkpoint-368] 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-1472\n","Configuration saved in bert-finetuned-comunicados/checkpoint-1472/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-1472/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-1472/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-1472/special_tokens_map.json\n","Deleting older checkpoint [bert-finetuned-comunicados/checkpoint-552] 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-1656\n","Configuration saved in bert-finetuned-comunicados/checkpoint-1656/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-1656/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-1656/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-1656/special_tokens_map.json\n","Deleting older checkpoint [bert-finetuned-comunicados/checkpoint-736] 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-1840\n","Configuration saved in bert-finetuned-comunicados/checkpoint-1840/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-1840/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-1840/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-1840/special_tokens_map.json\n","Deleting older checkpoint [bert-finetuned-comunicados/checkpoint-920] 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-2024\n","Configuration saved in bert-finetuned-comunicados/checkpoint-2024/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-2024/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-2024/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-2024/special_tokens_map.json\n","Deleting older checkpoint [bert-finetuned-comunicados/checkpoint-1104] 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-2208\n","Configuration saved in bert-finetuned-comunicados/checkpoint-2208/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-2208/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-2208/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-2208/special_tokens_map.json\n","Deleting older checkpoint [bert-finetuned-comunicados/checkpoint-1288] 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-2392\n","Configuration saved in bert-finetuned-comunicados/checkpoint-2392/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-2392/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-2392/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-2392/special_tokens_map.json\n","Deleting older checkpoint [bert-finetuned-comunicados/checkpoint-1472] 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-2576\n","Configuration saved in bert-finetuned-comunicados/checkpoint-2576/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-2576/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-2576/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-2576/special_tokens_map.json\n","Deleting older checkpoint [bert-finetuned-comunicados/checkpoint-1656] 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-2760\n","Configuration saved in bert-finetuned-comunicados/checkpoint-2760/config.json\n","Model weights saved in bert-finetuned-comunicados/checkpoint-2760/pytorch_model.bin\n","tokenizer config file saved in bert-finetuned-comunicados/checkpoint-2760/tokenizer_config.json\n","Special tokens file saved in bert-finetuned-comunicados/checkpoint-2760/special_tokens_map.json\n","Deleting older checkpoint [bert-finetuned-comunicados/checkpoint-1840] 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-2024 (score: 0.8748508565905666).\n"]},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=2760, training_loss=0.19438151589770247, metrics={'train_runtime': 4258.1121, 'train_samples_per_second': 20.657, 'train_steps_per_second': 1.296, 'total_flos': 1.157276708112384e+16, 'train_loss': 0.19438151589770247, 'epoch': 15.0})"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","source":["trainer.evaluate()"],"metadata":{"id":"CdaAeqNrLNS5","colab":{"base_uri":"https://localhost:8080/","height":283},"executionInfo":{"status":"ok","timestamp":1677881847297,"user_tz":180,"elapsed":8356,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"97643986-1740-4d1b-eb2a-35ed465cc123"},"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': 0.9395723938941956,\n"," 'eval_accuracy': 0.8806584362139918,\n"," 'eval_precision': 0.880405257406612,\n"," 'eval_recall': 0.8806584362139918,\n"," 'eval_f1': 0.8748508565905666,\n"," 'eval_runtime': 8.1639,\n"," 'eval_samples_per_second': 29.765,\n"," 'eval_steps_per_second': 1.96,\n"," 'epoch': 15.0}"]},"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":{"id":"T_0Bui8UQcdb","colab":{"base_uri":"https://localhost:8080/","height":106},"executionInfo":{"status":"ok","timestamp":1677881855578,"user_tz":180,"elapsed":8302,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"86b38c86-e3a2-4b76-daa9-e94ad902a1e8"},"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":{"id":"B0BLAeqoViDM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1677881855580,"user_tz":180,"elapsed":27,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"144dec2d-e4b9-4429-ad31-cfb3350edff1"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.93      0.93      0.93        15\n","           1       0.67      0.78      0.72        18\n","           2       0.75      1.00      0.86         3\n","           3       0.71      0.71      0.71         7\n","           4       0.95      0.97      0.96       114\n","           5       0.82      1.00      0.90         9\n","           6       1.00      0.75      0.86         4\n","           7       0.70      0.50      0.58        14\n","           8       0.50      1.00      0.67         4\n","           9       0.84      0.73      0.78        22\n","          10       0.83      0.71      0.77         7\n","          11       1.00      0.75      0.86         4\n","          12       0.95      0.82      0.88        22\n","\n","    accuracy                           0.87       243\n","   macro avg       0.82      0.82      0.81       243\n","weighted avg       0.88      0.87      0.87       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":{"id":"FkZNEKrAaHh3","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1677881857532,"user_tz":180,"elapsed":1973,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"935600fe-3478-434e-a6e5-3d6920f5f51a"},"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":["!pip install transformers_interpret"],"metadata":{"id":"e_7d3uwKC7AA","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1677881866854,"user_tz":180,"elapsed":9329,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"9cf05e79-9777-463d-b554-bf5b2b50d59f"},"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[31m5.3 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 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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":["trainer.save_model(model_path+\"/BERT_WL_balanced_70-15-15.mod\")\n","from transformers import AutoModelForSequenceClassification\n","modelo_carregador = AutoModelForSequenceClassification.from_pretrained(model_path+\"/BERT_WL_balanced_70-15-15.mod\")\n","##trainer.save_model(\"Trained model\")"],"metadata":{"id":"8S0hkaZGeot2","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1677881879592,"user_tz":180,"elapsed":12764,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"e2877034-5789-4322-e1f7-e391828e384d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Saving model checkpoint to drive/MyDrive/Mestrado/defesa/models/BERT_WL_balanced_70-15-15.mod\n","Configuration saved in drive/MyDrive/Mestrado/defesa/models/BERT_WL_balanced_70-15-15.mod/config.json\n","Model weights saved in drive/MyDrive/Mestrado/defesa/models/BERT_WL_balanced_70-15-15.mod/pytorch_model.bin\n","tokenizer config file saved in drive/MyDrive/Mestrado/defesa/models/BERT_WL_balanced_70-15-15.mod/tokenizer_config.json\n","Special tokens file saved in drive/MyDrive/Mestrado/defesa/models/BERT_WL_balanced_70-15-15.mod/special_tokens_map.json\n","loading configuration file drive/MyDrive/Mestrado/defesa/models/BERT_WL_balanced_70-15-15.mod/config.json\n","Model config BertConfig {\n","  \"_name_or_path\": \"drive/MyDrive/Mestrado/defesa/models/BERT_WL_balanced_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_balanced_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_balanced_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":["from transformers_interpret import SequenceClassificationExplainer\n","df=pd.DataFrame(dataset['test'])\n","df.iloc[19].text"],"metadata":{"id":"US-FvwyzDDQm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1677881880126,"user_tz":180,"elapsed":559,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"705c6058-eaa5-49fd-de1c-4a91f1cfd532"},"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":{"id":"V7DtI694DQLV","colab":{"base_uri":"https://localhost:8080/","height":531},"executionInfo":{"status":"ok","timestamp":1677882024528,"user_tz":180,"elapsed":144421,"user":{"displayName":"Fábio Cordeiro","userId":"00933077666862327455"}},"outputId":"46fca23d-17a6-46a1-dcc7-581892ed976b"},"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; margin-top: 5px;             padding-top: 5px; display: inline-block\"><b>Legend: </b><span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 60%)\"></span> Negative  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 100%)\"></span> Neutral  <span style=\"display: inline-block; width: 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inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(0, 75%, 100%)\"></span> Neutral  <span style=\"display: inline-block; width: 10px; height: 10px;                 border: 1px solid; background-color:                 hsl(120, 75%, 50%)\"></span> Positive  </div><tr><th>True Label</th><th>Predicted Label</th><th>Attribution Label</th><th>Attribution Score</th><th>Word Importance</th><tr><td><text style=\"padding-right:2em\"><b>1</b></text></td><td><text style=\"padding-right:2em\"><b>LABEL_1 (1.00)</b></text></td><td><text style=\"padding-right:2em\"><b>LABEL_1</b></text></td><td><text style=\"padding-right:2em\"><b>2.22</b></text></td><td><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> [CLS]                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font 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color=\"black\"> veio                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> do                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> t                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> ##ce                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> para                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> o                    </font></mark><mark style=\"background-color: hsl(120, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> 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</font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> estava                    </font></mark><mark style=\"background-color: hsl(0, 75%, 100%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> na                    </font></mark><mark style=\"background-color: hsl(120, 75%, 99%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> classificação                    </font></mark><mark style=\"background-color: hsl(120, 75%, 98%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> do                    </font></mark><mark style=\"background-color: hsl(120, 75%, 95%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> teste                    </font></mark><mark style=\"background-color: hsl(120, 75%, 92%); opacity:1.0;                     line-height:1.75\"><font color=\"black\"> sele                    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