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null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "2efae8e22a294bc5a29abedeb136f909": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "source": [ "# Instalacja pakietów" ], "metadata": { "id": "t2xXKpOpcZg_" } }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fdpN7ugfauLD", "outputId": "e16a56fb-980a-4b83-c12a-4231d68d9af2" }, "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\n", " Downloading transformers-4.26.1-py3-none-any.whl (6.3 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m71.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hCollecting datasets\n", " Downloading datasets-2.9.0-py3-none-any.whl (462 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m462.8/462.8 KB\u001b[0m \u001b[31m18.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.8/dist-packages (1.13.1+cu116)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers) (3.9.0)\n", "Requirement already satisfied: pyyaml>=5.1 in 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transformers, datasets\n", " Attempting uninstall: urllib3\n", " Found existing installation: urllib3 1.24.3\n", " Uninstalling urllib3-1.24.3:\n", " Successfully uninstalled urllib3-1.24.3\n", "Successfully installed datasets-2.9.0 huggingface-hub-0.12.0 multiprocess-0.70.14 responses-0.18.0 tokenizers-0.13.2 transformers-4.26.1 urllib3-1.26.14 xxhash-3.2.0\n" ] } ], "source": [ "!pip install transformers datasets torch" ] }, { "cell_type": "markdown", "source": [ "# Załadowanie pakietów" ], "metadata": { "id": "s8cfdy_6ldCn" } }, { "cell_type": "code", "source": [ "from datasets import load_dataset\n", "from transformers import GPT2Tokenizer\n", "import torch\n", "from torch.utils.data import TensorDataset, random_split\n", "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler\n", "from transformers import GPT2ForSequenceClassification, GPT2Config\n", "from transformers import get_linear_schedule_with_warmup\n", "import numpy as np\n", "import time\n", "import datetime\n", "import random" ], "metadata": { "id": "yLS_x9DIlgSs" }, "execution_count": 2, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Załadowanie datasetu\n", "sms_spam" ], "metadata": { "id": "fPwDyJd5cdaE" } }, { "cell_type": "code", "source": [ "dataset = load_dataset(\"sms_spam\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 244, "referenced_widgets": [ "4d28a819f7744b4ebcc6ff7da5438505", "f2923388d9fd4a819dab9e6294e8e663", "0660419f21e44cbd9d2a410b55412a8d", "670ac47a54c04b97beae23d43266a78f", "ba12be10705041c39f87cfb2151df859", "c9d3ee38f1dc42c5b688b06e98cfc751", "422332e84b4e4ce88eb8eae5a927f855", "ab1d5c664cb64ae3b5cdd27c6f8a6ecf", "5377f59cde4945a7b1b58f756a86b331", "d520817cd13c4e778d402fd26eca64af", "287518f909c045fbb8bb273e90299ff4", "a08b97bcaed348d6a719270ce42bce82", "f77d5fb080a34d3bae60e717963f7375", "f662c61ed7ab49cca8f49e787243c2a9", "9eb069bc0e66466fa235389126436554", "6cc13ab8ebb2457c9180ceecaa8305a1", "75924d8d3e6149488908fe85885c1632", "f5cfc5a4de0c41148d8395c8b5825f1a", "2ace0a0212e3420f8a7de04e70ecf6d1", "9e17280369874a82bfd83605d1938353", "c517ff5a95c941159950d5ff4f840b46", "c266cdb1e36f49899f59b18d760e86b3", "efb039646ac446448b73f9b110f078bc", "f1ff9911df224a409eb4dcd077105602", "601286de01af431cb06a95b3c52c1297", "82029075990a46549c287f70d96fb241", "9d92880154284cfabd34fdd2a879557e", "a902ce551fbf4d269f0af48e8d999456", "44d21dcb05e5432395b1571269ae3e3e", "6205009e4b634c5aba94decb5f0737d9", "fd09d4a2d52d43bb8dcb2ca277734d63", "b95e543b05f54ab69eebb5accf5f16f1", "58d38196f2cc4c71939c3023b86b8f66", "48e5b14d12c349d8971629d44439ba90", "3638a8adbb614c019bee2d196178300f", "939899827f79406abfebd4eb924a1f28", "0cec573cacdf4ce7abffb6b23530912a", "1b2509bf126a4b1995d3ce34bdff94bb", "725100fe32924048bd546b35c4c7e97b", "08532fee0806449b852d2abb6dcc800f", "7e13a9bc11754d81a1767648fba9bddd", "cc2a4f6dfbfd4d6b9b50c88007e8acb1", "36680638abcb432eaba702e0812e13b0", "98629adb6fe243f4a93d32feb910fe4a", "ad8aba6ae12b442d8f565736306f629d", "fbc3e452aaef4d9e8276672d79a16a26", "91df8c29dc38436a997b8699bf7529d5", "e318a73994954820bf4bf1605b85224e", "2ee14bfa717540979e2d96d3dfc38b3a", "3fd4cd502bc248cb93bb0edbd0d976d7", "cf583a214c4549ebb58e82cf82f7df75", "c24292f39f674a7ea0ecc2f955c2392c", "b2b9b9ef25d442db90aa7b48002de18e", "876ccb2fe56644609ad3028e8cc93909", "b96fae3e626f4d1a9977ae67bffde0a8", "5e0f638e84434178bf5f67ad2ebfece8", "cdadce8864194006ad83327ad98e18f7", "fff9b08154f54f98a4755e35dfb3fbb0", "b57ec86fc53242aca985994838c31489", "8be5e673687543fd9086c7ef58d903d0", "0b772ad96256464fb253a9417beb153b", "84806612e351433e8b05541d7d846e59", "8371b9441c7445049622c24a150170cb", "262e1abb42814751833ca2de1c2b8c9d", "7f16ec94084242db87907309d3b5ec4a", "f9c48d37ee844fa0872ab6315a8ee387" ] }, "id": "N1EWeM0KcYtO", "outputId": "79479873-3ccf-40a2-d8ec-77c486864036" }, "execution_count": 3, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading builder script: 0%| | 0.00/3.21k [00:005,} test samples'.format(test_size))\n", "print('{:>5,} training samples'.format(train_size))\n", "print('{:>5,} validation samples'.format(val_size))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vH3yXhA0hT3n", "outputId": "fd6c0545-d91a-4920-f961-d758cde83911" }, "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "1,000 test samples\n", "4,116 training samples\n", " 458 validation samples\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Create train and validation loaders" ], "metadata": { "id": "z1hVsejihpO2" } }, { "cell_type": "code", "source": [ "batch_size = 8\n", "\n", "train_dataloader = DataLoader(\n", " train_dataset,\n", " sampler = RandomSampler(train_dataset),\n", " batch_size = batch_size\n", " )\n", "\n", "validation_dataloader = DataLoader(\n", " val_dataset,\n", " sampler = SequentialSampler(val_dataset),\n", " batch_size = batch_size\n", " )" ], "metadata": { "id": "k4pXght6hre3" }, "execution_count": 10, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Device check" ], "metadata": { "id": "MnErwHAbl_rF" } }, { "cell_type": "code", "source": [ "if torch.cuda.is_available(): \n", " device = torch.device(\"cuda\")\n", "\n", " print('There are %d GPU(s) available.' % torch.cuda.device_count())\n", " print('We will use the GPU:', torch.cuda.get_device_name(0))\n", "\n", "else:\n", " print('No GPU available, using the CPU instead.')\n", " device = torch.device(\"cpu\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "aUvyBFxzmBUy", "outputId": "04bc746e-0d7a-443f-dfd2-df757b49cc04" }, "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "There are 1 GPU(s) available.\n", "We will use the GPU: Tesla T4\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Load GPT2 model" ], "metadata": { "id": "o-YrojT-iIfY" } }, { "cell_type": "code", "source": [ "model = GPT2ForSequenceClassification.from_pretrained(\n", " 'gpt2',\n", " num_labels = 2,\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 84, "referenced_widgets": [ "54a468d755ee4d398517315461829a70", "7d9cfb12850a48c1a9cfde1118adc2ea", "2534269fcfad4bc78214a4a606842072", "b1e3f817928c42e9b69db75289a9d30a", "91fd0a3f735a4d35a040f7bba80d3e24", "0ea13a9834954df78af18a82ac52593a", "65da66d6460c4973a5cf76d626105013", "1fc49833bbb74b358896cddf45f76efe", "2b5f3b65686a4794a54a96f48c03902c", "f009b8b012b24afab7b485ebb31129a5", "2efae8e22a294bc5a29abedeb136f909" ] }, "id": "sIP3VGZmiK9s", "outputId": "ebac6f6b-b0c4-49a3-8da7-2c98fe1bbbf1" }, "execution_count": 12, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading (…)\"pytorch_model.bin\";: 0%| | 0.00/548M [00:005,} of {:>5,}. Elapsed: {:}.'.format(step, len(train_dataloader), elapsed))\n", "\n", " b_input_ids = batch[0].to(device)\n", " b_input_mask = batch[1].to(device)\n", " b_labels = batch[2].to(device)\n", "\n", " model.zero_grad() \n", "\n", " outputs = model(b_input_ids, \n", " token_type_ids=None, \n", " attention_mask=b_input_mask, \n", " labels=b_labels)\n", "\n", " loss = outputs['loss']\n", " total_train_loss += loss.item()\n", "\n", " loss.backward()\n", " torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n", "\n", " optimizer.step()\n", " scheduler.step()\n", "\n", " avg_train_loss = total_train_loss / len(train_dataloader) \n", " training_time = format_time(time.time() - t0)\n", "\n", " print(\"\")\n", " print(\" Average training loss: {0:.2f}\".format(avg_train_loss))\n", " print(\" Training epcoh took: {:}\".format(training_time))\n", " \n", " # ========================================\n", " # Validation\n", " # ========================================\n", "\n", " print(\"\")\n", " print(\"Running Validation...\")\n", "\n", " t0 = time.time()\n", " model.eval()\n", "\n", " total_eval_accuracy = 0\n", " total_eval_loss = 0\n", " nb_eval_steps = 0\n", "\n", " for batch in validation_dataloader:\n", " b_input_ids = batch[0].to(device)\n", " b_input_mask = batch[1].to(device)\n", " b_labels = batch[2].to(device)\n", " \n", " with torch.no_grad(): \n", " outputs = model(b_input_ids, \n", " token_type_ids=None, \n", " attention_mask=b_input_mask,\n", " labels=b_labels)\n", " loss = outputs['loss']\n", " logits = outputs['logits']\n", " \n", " total_eval_loss += loss.item()\n", "\n", " logits = logits.detach().cpu().numpy()\n", " label_ids = b_labels.to('cpu').numpy()\n", "\n", " total_eval_accuracy += flat_accuracy(logits, label_ids)\n", " \n", " avg_val_accuracy = total_eval_accuracy / len(validation_dataloader)\n", " print(\" Accuracy: {0:.2f}\".format(avg_val_accuracy))\n", "\n", " avg_val_loss = total_eval_loss / len(validation_dataloader)\n", " validation_time = format_time(time.time() - t0)\n", " \n", " print(\" Validation Loss: {0:.2f}\".format(avg_val_loss))\n", " print(\" Validation took: {:}\".format(validation_time))\n", "\n", " training_stats.append(\n", " {\n", " 'epoch': epoch_i + 1,\n", " 'Training Loss': avg_train_loss,\n", " 'Valid. Loss': avg_val_loss,\n", " 'Valid. Accur.': avg_val_accuracy,\n", " 'Training Time': training_time,\n", " 'Validation Time': validation_time\n", " }\n", " )\n", "\n", "print(\"\")\n", "print(\"Training complete!\")\n", "\n", "print(\"Total training took {:} (h:mm:ss)\".format(format_time(time.time()-total_t0)))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hnq-2iztdYie", "outputId": "f2cf6703-9ab8-4dbf-e5d4-22f899a28776" }, "execution_count": 16, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "======== Epoch 1 / 4 ========\n", "Training...\n", " Batch 40 of 515. Elapsed: 0:00:19.\n", " Batch 80 of 515. Elapsed: 0:00:35.\n", " Batch 120 of 515. Elapsed: 0:00:52.\n", " Batch 160 of 515. Elapsed: 0:01:08.\n", " Batch 200 of 515. Elapsed: 0:01:25.\n", " Batch 240 of 515. Elapsed: 0:01:42.\n", " Batch 280 of 515. Elapsed: 0:01:58.\n", " Batch 320 of 515. Elapsed: 0:02:15.\n", " Batch 360 of 515. Elapsed: 0:02:32.\n", " Batch 400 of 515. Elapsed: 0:02:49.\n", " Batch 440 of 515. Elapsed: 0:03:06.\n", " Batch 480 of 515. Elapsed: 0:03:24.\n", "\n", " Average training loss: 0.14\n", " Training epcoh took: 0:03:38\n", "\n", "Running Validation...\n", " Accuracy: 0.97\n", " Validation Loss: 0.21\n", " Validation took: 0:00:08\n", "\n", "======== Epoch 2 / 4 ========\n", "Training...\n", " Batch 40 of 515. Elapsed: 0:00:17.\n", " Batch 80 of 515. Elapsed: 0:00:35.\n", " Batch 120 of 515. Elapsed: 0:00:52.\n", " Batch 160 of 515. Elapsed: 0:01:09.\n", " Batch 200 of 515. Elapsed: 0:01:27.\n", " Batch 240 of 515. Elapsed: 0:01:44.\n", " Batch 280 of 515. Elapsed: 0:02:01.\n", " Batch 320 of 515. Elapsed: 0:02:19.\n", " Batch 360 of 515. Elapsed: 0:02:36.\n", " Batch 400 of 515. Elapsed: 0:02:54.\n", " Batch 440 of 515. Elapsed: 0:03:11.\n", " Batch 480 of 515. Elapsed: 0:03:29.\n", "\n", " Average training loss: 0.04\n", " Training epcoh took: 0:03:44\n", "\n", "Running Validation...\n", " Accuracy: 0.97\n", " Validation Loss: 0.19\n", " Validation took: 0:00:08\n", "\n", "======== Epoch 3 / 4 ========\n", "Training...\n", " Batch 40 of 515. Elapsed: 0:00:17.\n", " Batch 80 of 515. Elapsed: 0:00:35.\n", " Batch 120 of 515. Elapsed: 0:00:52.\n", " Batch 160 of 515. Elapsed: 0:01:10.\n", " Batch 200 of 515. Elapsed: 0:01:27.\n", " Batch 240 of 515. Elapsed: 0:01:45.\n", " Batch 280 of 515. Elapsed: 0:02:02.\n", " Batch 320 of 515. Elapsed: 0:02:20.\n", " Batch 360 of 515. Elapsed: 0:02:37.\n", " Batch 400 of 515. Elapsed: 0:02:55.\n", " Batch 440 of 515. Elapsed: 0:03:12.\n", " Batch 480 of 515. Elapsed: 0:03:30.\n", "\n", " Average training loss: 0.03\n", " Training epcoh took: 0:03:45\n", "\n", "Running Validation...\n", " Accuracy: 0.97\n", " Validation Loss: 0.16\n", " Validation took: 0:00:08\n", "\n", "======== Epoch 4 / 4 ========\n", "Training...\n", " Batch 40 of 515. Elapsed: 0:00:17.\n", " Batch 80 of 515. Elapsed: 0:00:35.\n", " Batch 120 of 515. Elapsed: 0:00:52.\n", " Batch 160 of 515. Elapsed: 0:01:10.\n", " Batch 200 of 515. Elapsed: 0:01:27.\n", " Batch 240 of 515. Elapsed: 0:01:45.\n", " Batch 280 of 515. Elapsed: 0:02:02.\n", " Batch 320 of 515. Elapsed: 0:02:20.\n", " Batch 360 of 515. Elapsed: 0:02:37.\n", " Batch 400 of 515. Elapsed: 0:02:55.\n", " Batch 440 of 515. Elapsed: 0:03:12.\n", " Batch 480 of 515. Elapsed: 0:03:30.\n", "\n", " Average training loss: 0.01\n", " Training epcoh took: 0:03:45\n", "\n", "Running Validation...\n", " Accuracy: 0.98\n", " Validation Loss: 0.11\n", " Validation took: 0:00:08\n", "\n", "Training complete!\n", "Total training took 0:15:24 (h:mm:ss)\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Train summary" ], "metadata": { "id": "z3nngo5DgZe4" } }, { "cell_type": "code", "source": [ "import pandas as pd\n", "\n", "pd.set_option('precision', 2)\n", "df_stats = pd.DataFrame(data=training_stats)\n", "\n", "df_stats = df_stats.set_index('epoch')\n", "df_stats" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "qVSGSZ5-gbnV", "outputId": "b6e5d689-6748-4e0d-a43d-0484de05129d" }, "execution_count": 17, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Training Loss Valid. Loss Valid. Accur. Training Time Validation Time\n", "epoch \n", "1 0.14 0.21 0.97 0:03:38 0:00:08\n", "2 0.04 0.19 0.97 0:03:44 0:00:08\n", "3 0.03 0.16 0.97 0:03:45 0:00:08\n", "4 0.01 0.11 0.98 0:03:45 0:00:08" ], "text/html": [ "\n", "
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Training LossValid. LossValid. Accur.Training TimeValidation Time
epoch
10.140.210.970:03:380:00:08
20.040.190.970:03:440:00:08
30.030.160.970:03:450:00:08
40.010.110.980:03:450:00:08
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\n", " " ] }, "metadata": {}, "execution_count": 17 } ] }, { "cell_type": "code", "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "import seaborn as sns\n", "\n", "sns.set(style='darkgrid')\n", "\n", "sns.set(font_scale=1.5)\n", "plt.rcParams[\"figure.figsize\"] = (12,6)\n", "\n", "plt.plot(df_stats['Training Loss'], 'b-o', label=\"Training\")\n", "plt.plot(df_stats['Valid. Loss'], 'g-o', label=\"Validation\")\n", "\n", "plt.title(\"Training & Validation Loss\")\n", "plt.xlabel(\"Epoch\")\n", "plt.ylabel(\"Loss\")\n", "plt.legend()\n", "plt.xticks([1, 2, 3, 4])\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 427 }, "id": "qhDALEePhHp1", "outputId": "ddcab2bb-da52-4647-8d04-167993f6c98f" }, "execution_count": 18, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "# Create test loader" ], "metadata": { "id": "7gwWvjFwhJen" } }, { "cell_type": "code", "source": [ "prediction_dataloader = DataLoader(\n", " test_dataset,\n", " sampler = SequentialSampler(test_dataset),\n", " batch_size = batch_size\n", " )" ], "metadata": { "id": "du6qCdHyhMms" }, "execution_count": 19, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Evaluate on test dataset" ], "metadata": { "id": "n9E84sH2hOt7" } }, { "cell_type": "code", "source": [ "print('Predicting labels for {:,} test sentences...'.format(len(test_dataset)))\n", "\n", "model.eval()\n", "predictions , true_labels = [], []\n", "\n", "for batch in prediction_dataloader:\n", " batch = tuple(t.to(device) for t in batch)\n", " \n", " b_input_ids, b_input_mask, b_labels = batch\n", " \n", " with torch.no_grad():\n", " outputs = model(b_input_ids, token_type_ids=None, \n", " attention_mask=b_input_mask)\n", "\n", " logits = outputs['logits']\n", "\n", " logits = logits.detach().cpu().numpy()\n", " label_ids = b_labels.to('cpu').numpy()\n", "\n", " predictions.append(logits)\n", " true_labels.append(label_ids)\n", "\n", "print(' DONE.')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "s3nFSXgbhRs1", "outputId": "39a16e42-8d7e-4e31-95f1-e29118ce62f3" }, "execution_count": 20, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Predicting labels for 1,000 test sentences...\n", " DONE.\n" ] } ] }, { "cell_type": "code", "source": [ "results_ok = 0\n", "results_false = 0\n", "for idx, true_labels_batch in enumerate(true_labels):\n", " predictions_i = np.argmax(predictions[idx], axis=1).flatten()\n", " for bidx, true_label in enumerate(true_labels_batch):\n", " if true_label == predictions_i[bidx]:\n", " results_ok += 1\n", " else:\n", " results_false += 1\n", "\n", "print(\"Correct predictions: {}, incorrect results: {}, accuracy: {}\".format(results_ok, results_false, float(results_ok) / (results_ok + results_false)))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "eNMYIt7RhWYM", "outputId": "7257f066-6539-4e42-d0ae-e6c5609f1812" }, "execution_count": 21, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Correct predictions: 990, incorrect results: 10, accuracy: 0.99\n" ] } ] }, { "cell_type": "markdown", "source": [ "# MCC Score" ], "metadata": { "id": "SwHJwpqKhZ51" } }, { "cell_type": "code", "source": [ "from sklearn.metrics import matthews_corrcoef\n", "\n", "matthews_set = []\n", "print('Calculating Matthews Corr. Coef. for each batch...')\n", "\n", "for i in range(len(true_labels)):\n", " pred_labels_i = np.argmax(predictions[i], axis=1).flatten()\n", " \n", " matthews = matthews_corrcoef(true_labels[i], pred_labels_i) \n", " matthews_set.append(matthews)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "oqfHjUXThb2J", "outputId": "2bbfcaeb-5ea8-498e-a5a0-8f2fac83feea" }, "execution_count": 22, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Calculating Matthews Corr. Coef. for each batch...\n" ] } ] }, { "cell_type": "code", "source": [ "ax = sns.barplot(x=list(range(len(matthews_set))), y=matthews_set, ci=None)\n", "\n", "plt.title('MCC Score per Batch')\n", "plt.ylabel('MCC Score (-1 to +1)')\n", "plt.xlabel('Batch #')\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 427 }, "id": "JJoRzvr0hePf", "outputId": "ebc78102-65e6-4847-d3c0-d3825870dc78" }, "execution_count": 23, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "flat_predictions = np.concatenate(predictions, axis=0)\n", "flat_predictions = np.argmax(flat_predictions, axis=1).flatten()\n", "\n", "flat_true_labels = np.concatenate(true_labels, axis=0)\n", "\n", "mcc = matthews_corrcoef(flat_true_labels, flat_predictions)\n", "\n", "print('Total MCC: %.3f' % mcc)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "8XER3sOFhfny", "outputId": "77ec6114-8ab3-4abd-c7b7-de95528a2bef" }, "execution_count": 24, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Total MCC: 0.960\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Save model" ], "metadata": { "id": "ZTd3f1yKhhkP" } }, { "cell_type": "code", "source": [ "from google.colab import drive\n", "\n", "drive.mount('/content/gdrive/', force_remount=True)\n", "\n", "output_dir = '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/GPT2_Model'\n", "print(\"Saving model to %s\" % output_dir)\n", "\n", "model_to_save = model.module if hasattr(model, 'module') else model\n", "model_to_save.save_pretrained(output_dir)\n", "tokenizer.save_pretrained(output_dir)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gqSUWqCqhizx", "outputId": "76d1febd-031d-456a-b108-7b664b2b5729" }, "execution_count": 25, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/gdrive/\n", "Saving model to /content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/GPT2_Model\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "('/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/GPT2_Model/tokenizer_config.json',\n", " '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/GPT2_Model/special_tokens_map.json',\n", " '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/GPT2_Model/vocab.json',\n", " '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/GPT2_Model/merges.txt',\n", " '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/GPT2_Model/added_tokens.json')" ] }, "metadata": {}, "execution_count": 25 } ] }, { "cell_type": "markdown", "source": [ "# Bibliografia\n", "- https://gmihaila.github.io/tutorial_notebooks/gpt2_finetune_classification/\n", "- https://mccormickml.com/2019/07/22/BERT-fine-tuning/#a1-saving--loading-fine-tuned-model" ], "metadata": { "id": "Er-thm7dkbIW" } } ] }