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"outputs": [ @@ -3661,7 +3660,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "26f1d5f367aa49c9a572adcded83219a" + "model_id": "1877adc7cb1a4525b016c4b623b0e26c" } }, "metadata": {} @@ -3675,7 +3674,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "fc229165f10d40d59c6d751a13848f6d" + "model_id": "cdeea076963c4466805f2db30f8dadae" } }, "metadata": {} @@ -3689,7 +3688,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "39e2d7303cdf4d02bf2d31352a6e1e46" + "model_id": "67f9ea537f544a67bd791cd7afb931ce" } }, "metadata": {} @@ -3710,7 +3709,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "1b3b7113964f4323ba191fcf81234c9e" + "model_id": "ed5b0b0708de49f5ba826e4e72e51e0c" } }, "metadata": {} @@ -3724,7 +3723,7 @@ "application/vnd.jupyter.widget-view+json": { 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"Z6cC0YjAhmw_" } }, + { + "cell_type": "code", + "source": [ + "def check_class_balance(dataset):\n", + " spam_count = 0.0\n", + " not_spam_count = 0.0\n", + " for row in dataset:\n", + " if row[2].item() == 1:\n", + " spam_count += 1.0\n", + " else:\n", + " not_spam_count += 1.0\n", + " return spam_count / not_spam_count " + ], + "metadata": { + "id": "IhwCBeAjvGdm" + }, + "execution_count": 9, + "outputs": [] + }, { "cell_type": "code", "source": [ "dataset = TensorDataset(input_ids, attention_masks, labels)\n", + "print(\"Spam to not spam messages ratio: {}\\n\".format(check_class_balance(dataset)))\n", "\n", "test_size = 1000\n", "dataset_len = len(dataset)\n", @@ -4049,25 +4069,36 @@ "test_dataset, train_dataset, val_dataset = random_split(dataset, [test_size, train_size, val_size])\n", "\n", "print('{:>5,} test samples'.format(test_size))\n", + "print(\"Ratio: {}\\n\".format(check_class_balance(test_dataset)))\n", "print('{:>5,} training samples'.format(train_size))\n", - "print('{:>5,} validation samples'.format(val_size))" + "print(\"Ratio: {}\\n\".format(check_class_balance(train_dataset)))\n", + "print('{:>5,} validation samples'.format(val_size))\n", + "print(\"Ratio: {}\\n\".format(check_class_balance(val_dataset)))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vH3yXhA0hT3n", - "outputId": "1023a01b-60b0-4de0-ba97-28518b935a21" + "outputId": "f03cb870-88ba-4adc-b85c-ece5a704060a" }, - "execution_count": 9, + "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ + "Spam to not spam messages ratio: 0.15475450590428838\n", + "\n", "1,000 test samples\n", + "Ratio: 0.17370892018779344\n", + "\n", "4,116 training samples\n", - " 458 validation samples\n" + "Ratio: 0.149399609047752\n", + "\n", + " 458 validation samples\n", + "Ratio: 0.16243654822335024\n", + "\n" ] } ] @@ -4101,7 +4132,7 @@ "metadata": { "id": "k4pXght6hre3" }, - "execution_count": 10, + "execution_count": null, 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"application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "ae89e755c6e64ab2a3101913d08820ef" + "model_id": "7a479cffc6a644c2b50a696ff72d4e8e" } }, "metadata": {} @@ -4719,7 +4718,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "db79fbdb9e25432a8f95221cc0bed971" + "model_id": "0cad760c82744f45a29dbf591953629d" } }, "metadata": {} @@ -4733,7 +4732,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "6fd330314de74ccc983da44783bcac7d" + "model_id": "2181a8b3b8f349c4803784c65fa7f6d2" } }, "metadata": {} @@ -4754,7 +4753,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "99cbe58514e3460398f032b93a57d068" + "model_id": "02ae7dbb11194f0e962ef177ef7b07fb" } }, "metadata": {} @@ -4771,7 +4770,7 @@ "base_uri": "https://localhost:8080/" }, "id": "JKFHPko3OnAV", - "outputId": 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"f2yGhxu80__k" + }, + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "parsed_dataset = parsed_dataset[1:123] + parsed_dataset[124:2000] + parsed_dataset[2001:3002] + parsed_dataset[3003:]\n", + "print(\"Spam to not spam messages ratio: {}\\n\".format(check_class_balance(parsed_dataset)))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "voYL96Ac1MQL", + "outputId": "bef4b6f9-7b4f-49e0-ac96-c8797a4ecc97" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Spam to not spam messages ratio: 0.1544041450777202\n", + "\n" + ] + } + ] + }, { "cell_type": "markdown", "source": [ @@ -5099,7 +5142,7 @@ "metadata": { "id": "Eu-7Eed8WgN0" }, - "execution_count": 11, + "execution_count": null, "outputs": [] }, { @@ -5152,7 +5195,7 @@ "id": "JKv9O8kfV2zZ", "outputId": "c902fc89-7c47-4eca-da89-411bc5b5e501" }, - "execution_count": 12, + "execution_count": null, 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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 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": 263, + "referenced_widgets": [ + "562b2df63fee469eab344732405d0341", + "f90233e79de4458fb6e5e4414d1f319f", + "da72fb505ad3414680bd29008dc8a285", + "6318027214494b38a5145e1fc2dded91", + "b96a09347ffe417da8bf8161b4d35c3b", + "c96b32196ffe4fb78199fa8c856ecda0", + "e02dd4691fcb4fd7a38db769acd9e675", + "f1cdfa643b024356909426c57e6c7f6c", + "70162009566c46f5ac4680cc13907e8b", + "28f4807f01df47fc86c2e1bf3f12848a", + "d653b1742fbc40e28fc1a0390c4392c8", + "5d658fd2b4ca452981f37d78e5a839ac", + "a08f108560f445389475c3c9816053dc", + "ed00365457a8496385e99d3b8545e71f", + "179a41119493452c9af8bd4a2591d47c", + "615083b133e84ea0abc862c2945b0a4c", + "2220b6b5230447018481fd546a0f8c30", + "1a35f271378e4d8f84147a39dc311cb5", + "e20d4fbfae75456da6c44ab2f4834ddf", + "d074053ea5d14c58a1209ce99688178d", + "eb3237ab419c44ee8b9a56543cdc1186", + "e4d702bd0e074d009d4a86f0d872fa97", + "1a84c9f0d4c34ab9bfb8e6c1632e1aab", + "4d1fbcd786ea4724a8302f6fa373a4c5", + "c4262c9d665c43fc95f5d651272e05ac", + "eaa840ba44e5468d863d701b7a887cb0", + "836cc5fb2aac4814975222e4cb3eb509", + "cc0ff055c7e348bfb7442e1da9c2d283", + "453451afd5a041cf8bd7b9bebae95e1a", + "e377724de4ea48dcb3a35d35100f77c2", + "2fa10546eb11424eb1376773fd6ce8c3", + "0b03f0f260f142b6a6333f234ba7b64a", + "21e668a45d514c51b87d5888bc0d3f04", + "94bed16fdbbe42ab89e2417d0bc2f6be", + "ea9d739338bc46e7b6aa9edfc90db7ee", + "cc4d58df01594e258fede64937001ccc", + "bdf2b2471bec4a519b62171990b90751", + "49298f7943c2412f891932c2a62598b3", + "f92d4941bc304dbfbaec3a15954a2216", + "17fad956c06a43a7bb57313168431b2f", + "5ab0d18b923d4918ba0a3d9de7398235", + "9838107a69dc444084a8cbc5a97c69b5", + "0f52a0bf45bf4840b74f50ef0e1ed104", + "a7669275b5474a45b79e810a51fa773c", + "98405dc577ee4a5e8827123916074b67", + "19b3e634059f478986f5dfb4ab34875e", + "4a3004ebe922475d9476d91e50ddb0c4", + "00ccecda328f47408f98dfbb498c241a", + "50a6f2294627414f80816e08c425d997", + "1d994423cee14277899cf0e2e21252a4", + "7a1a268464a5405bb96d2cd7f1f4a411", + "c8390fdbd4824593a5ca3505b3ff7481", + "fb35b00db49e4fb49199ffd6c5b1c1f0", + "e82ebc90a3884176a59c903e746b6728", + "ba525ced60344562922dfcd1563b295e", + "dcb1f03aab3e47bcaef73337578e90aa", + "00c77aa4d05c46939141db3fc8ec4e0c", + "81e81ff88979470190cbf98ad198fded", + "4a2a80e4c40c49d383472d2161b8397f", + "7f1c2ba12eba4f94b6b763701c7b83fc", + "3cfc5529a1e844a5876336436c787062", + "bbaf7eb0bfdc47bea197e32ede2bd5df", + "7040bba8c540494d8fd3eb09e4aa87f4", + "7b645af074de4ce89e521c4e32aaf790", + "049cd54da01a47d29fcbfadcb31e1122", + "707d4cadc9ae45a998d4db6f022b860e" + ] + }, + "id": "N1EWeM0KcYtO", + "outputId": "2b89ef33-01b2-4bc2-b217-fcd1464c1da9" + }, + "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(\"Ratio: {}\\n\".format(check_class_balance(test_dataset)))\n", + "print('{:>5,} training samples'.format(train_size))\n", + "print(\"Ratio: {}\\n\".format(check_class_balance(train_dataset)))\n", + "print('{:>5,} validation samples'.format(val_size))\n", + "print(\"Ratio: {}\\n\".format(check_class_balance(val_dataset)))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vH3yXhA0hT3n", + "outputId": "0ec353c2-39c6-4581-9a9c-375709ed7b14" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Spam to not spam messages ratio: 0.15475450590428838\n", + "\n", + "1,000 test samples\n", + "Ratio: 0.13895216400911162\n", + "\n", + "4,116 training samples\n", + "Ratio: 0.16074450084602368\n", + "\n", + " 458 validation samples\n", + "Ratio: 0.13647642679900746\n", + "\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": 11, + "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": "dff6ae93-d79c-4d97-e6f1-91bd533cc167" + }, + "execution_count": 12, + "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": [ + "# Create custom GPT2 model\n", + "Compared to GPT2ForSeqienceClassification - 2 additional linear layers.\n", + "https://github.com/huggingface/transformers/blob/bd469c40659ce76c81f69c7726759d249b4aef49/src/transformers/models/gpt2/modeling_gpt2.py#L1328" + ], + "metadata": { + "id": "o-YrojT-iIfY" + } + }, + { + "cell_type": "code", + "source": [ + "from transformers import GPT2ForSequenceClassification, GPT2Config, GPT2Model\n", + "from torch import nn" + ], + "metadata": { + "id": "QxEE2YTIr7L0" + }, + "execution_count": 13, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# in original model - no custom head just linear layer\n", + "# self.score = nn.Linear(config.n_embd, self.num_labels, bias=False)\n", + "\n", + "class GPT2ClassificationHeadCustom(nn.Module):\n", + " def __init__(self, config):\n", + " super().__init__()\n", + " self.dense_1 = nn.Linear(config.n_embd, config.n_embd)\n", + " self.dense_2 = nn.Linear(config.n_embd, config.n_embd)\n", + " self.dense_3 = nn.Linear(config.n_embd, config.num_labels)\n", + "\n", + " def forward(self, x):\n", + " x = self.dense_1(x)\n", + " x = torch.relu(x)\n", + "\n", + " x = self.dense_2(x)\n", + " x = torch.relu(x)\n", + "\n", + " x = self.dense_3(x)\n", + " x = torch.relu(x)\n", + " return x\n", + "\n", + "class GPT2ForSequenceClassificationCustom(GPT2ForSequenceClassification):\n", + " def __init__(self, config):\n", + " super().__init__(config)\n", + " self.num_labels = config.num_labels\n", + " self.transformer = GPT2Model(config)\n", + " self.score = GPT2ClassificationHeadCustom(config)\n", + "\n", + " self.init_weights()\n", + "\n", + " # Model parallel\n", + " self.model_parallel = False\n", + " self.device_map = None\n", + "\n", + "model = GPT2ForSequenceClassificationCustom.from_pretrained(\n", + " 'gpt2',\n", + " num_labels = 2,\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 103, + "referenced_widgets": [ + "e18707f0d4be4cf1bf2fd448c465a49a", + "8f9ca17cef4b491fab0ba086eddeee06", + "291394dabaf4461abc7d116c34a62972", + "79d4d0a4e6914963988862a457fe5004", + "3fb7ae428a654adb9639dca54c8487ca", + "c5a0d019aa454525b975be7b8125af52", + "bd2d006b7df94f52be982c93e6591ed9", + "8cf28f0e3d22453f9b1293f7309373a1", + "2ffeb0a10a69433689ca87de39f760b6", + "d2183878e1b343abb962b16a2615c0a5", + "68e1e4d6f5ce456398f333041eb00151" + ] + }, + "id": "sIP3VGZmiK9s", + "outputId": "e944fc5f-2abb-4c2f-d201-cbc8fc98e976" + }, + "execution_count": 14, + "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": "36eeffb8-2aaa-4db8-c2d3-17e7addaa693" + }, + "execution_count": 18, + "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: 0:00:36.\n", + " Batch 120 of 515. Elapsed: 0:00:53.\n", + " Batch 160 of 515. Elapsed: 0:01:11.\n", + " Batch 200 of 515. Elapsed: 0:01:28.\n", + " Batch 240 of 515. Elapsed: 0:01:45.\n", + " Batch 280 of 515. Elapsed: 0:02:02.\n", + " Batch 320 of 515. Elapsed: 0:02:19.\n", + " Batch 360 of 515. Elapsed: 0:02:36.\n", + " Batch 400 of 515. Elapsed: 0:02:53.\n", + " Batch 440 of 515. Elapsed: 0:03:10.\n", + " Batch 480 of 515. Elapsed: 0:03:27.\n", + "\n", + " Average training loss: 0.09\n", + " Training epcoh took: 0:03:42\n", + "\n", + "Running Validation...\n", + " Accuracy: 0.99\n", + " Validation Loss: 0.03\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: 0:00:34.\n", + " Batch 120 of 515. Elapsed: 0:00:51.\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: 0:01:59.\n", + " Batch 320 of 515. Elapsed: 0:02:16.\n", + " Batch 360 of 515. Elapsed: 0:02:33.\n", + " Batch 400 of 515. Elapsed: 0:02:50.\n", + " Batch 440 of 515. Elapsed: 0:03:07.\n", + " Batch 480 of 515. Elapsed: 0:03:24.\n", + "\n", + " Average training loss: 0.04\n", + " Training epcoh took: 0:03:39\n", + "\n", + "Running Validation...\n", + " Accuracy: 0.99\n", + " Validation Loss: 0.04\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: 0:00:34.\n", + " Batch 120 of 515. Elapsed: 0:00:51.\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: 0:01:59.\n", + " Batch 320 of 515. Elapsed: 0:02:16.\n", + " Batch 360 of 515. Elapsed: 0:02:33.\n", + " Batch 400 of 515. Elapsed: 0:02:50.\n", + " Batch 440 of 515. Elapsed: 0:03:07.\n", + " Batch 480 of 515. Elapsed: 0:03:24.\n", + "\n", + " Average training loss: 0.02\n", + " Training epcoh took: 0:03:39\n", + "\n", + "Running Validation...\n", + " Accuracy: 0.99\n", + " Validation Loss: 0.04\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: 0:00:34.\n", + " Batch 120 of 515. Elapsed: 0:00:51.\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: 0:01:59.\n", + " Batch 320 of 515. Elapsed: 0:02:16.\n", + " Batch 360 of 515. Elapsed: 0:02:33.\n", + " Batch 400 of 515. Elapsed: 0:02:50.\n", + " Batch 440 of 515. Elapsed: 0:03:07.\n", + " Batch 480 of 515. Elapsed: 0:03:24.\n", + "\n", + " Average training loss: 0.01\n", + " Training epcoh took: 0:03:39\n", + "\n", + "Running Validation...\n", + " Accuracy: 0.99\n", + " Validation Loss: 0.04\n", + " Validation took: 0:00:08\n", + "\n", + "Training complete!\n", + "Total training took 0:15:09 (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": 204 + }, + "id": "qVSGSZ5-gbnV", + "outputId": "ecc3e8b1-e4cb-4f36-c90b-2802f4ce31a1" + }, + "execution_count": 19, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Training Loss Valid. Loss Valid. Accur. Training Time Validation Time\n", + "epoch \n", + "1 9.19e-02 0.03 0.99 0:03:42 0:00:08\n", + "2 3.62e-02 0.04 0.99 0:03:39 0:00:08\n", + "3 1.78e-02 0.04 0.99 0:03:39 0:00:08\n", + "4 8.69e-03 0.04 0.99 0:03:39 0:00:08" + ], + "text/html": [ + "\n", + "
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 19 + } + ] + }, + { + "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": "b3b86c25-2525-47ff-a65c-37abc3080da3" + }, + "execution_count": 20, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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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": 21, + "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": "81c4eefb-1d2a-4007-8a91-797578e496a8" + }, + "execution_count": 22, + "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": "08ce9721-edd9-41e0-dd15-2b9620283f37" + }, + "execution_count": 23, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Correct predictions: 991, incorrect results: 9, accuracy: 0.991\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": "c74b1bfe-1c97-4cc5-f0c4-e9a8cc45da28" + }, + "execution_count": 24, + "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": "38fe5dad-17bb-4e0b-839b-2d69d42a9e1c" + }, + "execution_count": 25, + "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": "b59467dc-3459-42a4-da12-f8abc5be4f95" + }, + "execution_count": 26, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Total MCC: 0.958\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_custom_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": "22b21cb7-8401-4b58-eb64-c33af55b9de7" + }, + "execution_count": 27, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/gdrive/\n", + "Saving model to /content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/GPT2_custom_model\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "('/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/GPT2_custom_model/tokenizer_config.json',\n", + " '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/GPT2_custom_model/special_tokens_map.json',\n", + " '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/GPT2_custom_model/vocab.json',\n", + " '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/GPT2_custom_model/merges.txt',\n", + " '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/GPT2_custom_model/added_tokens.json')" + ] + }, + "metadata": {}, + "execution_count": 27 + } + ] + }, + { + "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" + } + } + ] +} \ No newline at end of file diff --git a/projekt/GPT2_sms_spam.ipynb b/projekt/GPT2_sms_spam.ipynb index 0165961..2665154 100644 --- a/projekt/GPT2_sms_spam.ipynb +++ b/projekt/GPT2_sms_spam.ipynb @@ -15,3084 +15,6 @@ "gpuClass": "standard", "widgets": { "application/vnd.jupyter.widget-state+json": { - "4d28a819f7744b4ebcc6ff7da5438505": { - "model_module": 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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" + "Successfully installed datasets-2.9.0 huggingface-hub-0.12.1 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" ] } ], @@ -3577,78 +3576,78 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 244, + "height": 263, "referenced_widgets": [ - "4d28a819f7744b4ebcc6ff7da5438505", - "f2923388d9fd4a819dab9e6294e8e663", - "0660419f21e44cbd9d2a410b55412a8d", - "670ac47a54c04b97beae23d43266a78f", - "ba12be10705041c39f87cfb2151df859", - "c9d3ee38f1dc42c5b688b06e98cfc751", - "422332e84b4e4ce88eb8eae5a927f855", - "ab1d5c664cb64ae3b5cdd27c6f8a6ecf", - "5377f59cde4945a7b1b58f756a86b331", - "d520817cd13c4e778d402fd26eca64af", - "287518f909c045fbb8bb273e90299ff4", - "a08b97bcaed348d6a719270ce42bce82", - "f77d5fb080a34d3bae60e717963f7375", - "f662c61ed7ab49cca8f49e787243c2a9", - 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"version_major": 2, "version_minor": 0, - "model_id": "a08b97bcaed348d6a719270ce42bce82" + "model_id": "39cf5197378d4a4eb3963a4d16709519" } }, "metadata": {} @@ -3689,7 +3688,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "efb039646ac446448b73f9b110f078bc" + "model_id": "420a040cfaac4999b3ed5995c196a986" } }, "metadata": {} @@ -3710,7 +3709,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "48e5b14d12c349d8971629d44439ba90" + "model_id": "7476faf522e443a9bcc1e7d76c113a88" } }, "metadata": {} @@ -3724,7 +3723,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "ad8aba6ae12b442d8f565736306f629d" + "model_id": "f8917169c81e4ab6a5e88c0b432745db" } }, "metadata": {} @@ -3745,7 +3744,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "5e0f638e84434178bf5f67ad2ebfece8" + "model_id": "8d0715b6bb094b9fa14a7064e989ef87" } }, "metadata": {} @@ -3762,7 +3761,7 @@ "base_uri": "https://localhost:8080/" }, "id": "Mf1QIM_dlp2x", - "outputId": "352d6bf6-c5fb-4a20-a653-18fdc5a8610f" + "outputId": "3e9307f6-cbf5-498c-c1e5-48fd0c15957f" }, "execution_count": 4, "outputs": [ @@ -3800,42 +3799,42 @@ "base_uri": "https://localhost:8080/", "height": 113, "referenced_widgets": [ - "c66f887ac2b347f790613aad99628ddd", - "48d2febefb344765ba898f66631a20b5", - "ddff15d28609430e9901c59405a081f8", - "f3419ef8f62d4229a001e085c02af151", - "d08b4d68348c464cbc9b14d747786fde", - "ea925b20bb6442bcb126a4c152c46c44", - "b2df53a7ccb845d6bb9319daecc1a475", - "ee7bf6b4fbdd44ec99028b45184b9c31", - "ac3375bb5681453d8d5d711fbefd4455", - "39e1df93a3e04d949432e0f12e67bb53", - "0257dac8f585464daf84fc253f00a156", - "e13ede98e6d84b3db1ffed7075db1833", - "99c7e79fb47b46bd94532cd4fe31a154", - "344ea5fcf09c4c6992e7fd875a24d468", - "2250f533e2914c52827ba6eca0920c97", - "94e6951304cc4f74bdfb53b77ba02308", - "a3329ed7cefd4376956e44c7e04de4d5", - "a55ce911dc3b43c1bfc830496f1cb6d4", - "ae46982104714a43ba1bd2c5c6a76ff8", - "ec15d18ce87a4a7682772debeb9a37ae", - "b6a13356c2fc4ba09e599d6038625f80", - "2b24cf0161df478d91710a61af75aab9", - "1c8e2e670fe94e11905fdb82d5c28aef", - "88d4f4991f5846639b9216163cf928d7", - "5d13be7e7854411b84110c6c6aaf6aa7", - "f1adbed46a7d458ba893ca4e3620b275", - "72882edb5601464f8c3b6a76fd1c10d7", - "f27b9257a51f44a1aa816f88e407323b", - "124f164056d542f2820c1ebb99faeb18", - "ed544ca048f34d01bffd01109a2dd3e5", - "904ac8e80dae40f1a4f344a3c32f98cb", - "e8fbe6c23d84418eb265ebd38dd43007", - "3e98702f338c4c7ea96ce4fc466e6c52" + "8fceb26a6d2747e899537d28e1a7f819", + "1e1594b1d7ff495ebaf1055b7a5796b9", + "3d06186e5c35452c9f805ef332c8e963", + "f66e20dcbc964485929896e8e5d2929b", + "eb554373fa644b38a4cca04c9178a0a1", + "69a3b44309624139afa25663234dac82", + "bdbce0cabc9c4f6e8caef2ed48d6dd54", + "392d338529cb49a59b2afb1ebf0aff9a", + "90ab44c79e594a868fce9f16747f53d9", + "936f5b9e19354f44a4cb9c4477131405", + "1f34679a31554a6a91ae2db024361869", + "bd05251722ac43a0bf4dfe4958ea61a4", + "79ad467ff6f84d9f92a14b6ea41d7002", + "0ef61bcf73974c77bdd9a4114b944070", + "85aba1e0f89148e5abaa4247d9a62d15", + "713cd8fdb42c48d48142032a6948a3b7", + "66ca513a92e04e9aafd22286fef64427", + "7debd2aa4b5140dbaff4528961b4714d", + "5032b90243bd4b67bf12ee696112c4d5", + "bdf13c209a2043388a5b1c648ccf4179", + "e841fe2854bf4f6b9b7021ba91f5b002", + "d1d8838cc62e4684b2e0fdeb2e932829", + "b1b1058975a64c08bfe57e87b9392a5f", + "6f7eab6077ae4cdbbf66e90818c2e99d", + "e98d7df786314429a697f99160a8e724", + "c1ea782c1e484cee86d88b65acdf6646", + "d74bdb969f2d419197ff72fbf528f315", + "81bf5d95f33a422591a2a7a693753909", + "eb50a4ad2ff2453dbc2010cd7d449464", + "42ceba12957b43108d420f9c04cd6bd1", + "83b8eb341a9d40b59a6a562221ef4241", + "21534596456144a1ab256f86eaaef343", + "efc6356942f2489d97f6704c7b0d1c94" ] }, - "outputId": "8cdcb1d1-86b6-4a4b-98a4-7da2f341fa8b" + "outputId": "67151b75-dd52-405c-fdab-9cd67629e23f" }, "execution_count": 5, "outputs": [ @@ -3848,7 +3847,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "c66f887ac2b347f790613aad99628ddd" + "model_id": "8fceb26a6d2747e899537d28e1a7f819" } }, "metadata": {} @@ -3862,7 +3861,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "e13ede98e6d84b3db1ffed7075db1833" + "model_id": "bd05251722ac43a0bf4dfe4958ea61a4" } }, "metadata": {} @@ -3876,7 +3875,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "1c8e2e670fe94e11905fdb82d5c28aef" + "model_id": "b1b1058975a64c08bfe57e87b9392a5f" } }, "metadata": {} @@ -3896,7 +3895,7 @@ "base_uri": "https://localhost:8080/" }, "id": "ZxigrpcQdWCF", - "outputId": "cf3cd54a-24a7-45db-c1b4-dd772fb10bbf" + "outputId": "bc440b70-46ed-4ae0-9e6a-6d69a707778e" }, "execution_count": 6, "outputs": [ @@ -3938,7 +3937,7 @@ "height": 34 }, "id": "cmUVPrQYez3J", - "outputId": "bdea41f3-e599-4387-ef95-2fb5b9a3737d" + "outputId": "c0829832-7a6d-4019-8982-c63d0022b42d" }, "execution_count": 7, "outputs": [ @@ -3992,7 +3991,7 @@ "base_uri": "https://localhost:8080/" }, "id": "4u03dIS1fbKU", - "outputId": "3940e7ff-aed0-4689-93ff-4298ebfc4db4" + "outputId": "fe0e0eab-fda3-4a72-a8b5-e2a48212fb78" }, "execution_count": 8, "outputs": [ @@ -4034,16 +4033,37 @@ { "cell_type": "markdown", "source": [ - "# Split dataset" + "# Split dataset\n", + "Class balance ratio should be similar to base dataset ratio." ], "metadata": { "id": "Z6cC0YjAhmw_" } }, + { + "cell_type": "code", + "source": [ + "def check_class_balance(dataset):\n", + " spam_count = 0.0\n", + " not_spam_count = 0.0\n", + " for row in dataset:\n", + " if row[2].item() == 1:\n", + " spam_count += 1.0\n", + " else:\n", + " not_spam_count += 1.0\n", + " return spam_count / not_spam_count " + ], + "metadata": { + "id": "83DeMQwJvuMA" + }, + "execution_count": 9, + "outputs": [] + }, { "cell_type": "code", "source": [ "dataset = TensorDataset(input_ids, attention_masks, labels)\n", + "print(\"Spam to not spam messages ratio: {}\\n\".format(check_class_balance(dataset)))\n", "\n", "test_size = 1000\n", "dataset_len = len(dataset)\n", @@ -4053,25 +4073,36 @@ "test_dataset, train_dataset, val_dataset = random_split(dataset, [test_size, train_size, val_size])\n", "\n", "print('{:>5,} test samples'.format(test_size))\n", + "print(\"Ratio: {}\\n\".format(check_class_balance(test_dataset)))\n", "print('{:>5,} training samples'.format(train_size))\n", - "print('{:>5,} validation samples'.format(val_size))" + "print(\"Ratio: {}\\n\".format(check_class_balance(train_dataset)))\n", + "print('{:>5,} validation samples'.format(val_size))\n", + "print(\"Ratio: {}\\n\".format(check_class_balance(val_dataset)))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vH3yXhA0hT3n", - "outputId": "fd6c0545-d91a-4920-f961-d758cde83911" + "outputId": "8905beef-086b-459b-aa91-99bf4c448017" }, - "execution_count": 9, + "execution_count": 10, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ + "Spam to not spam messages ratio: 0.15475450590428838\n", + "\n", "1,000 test samples\n", + "Ratio: 0.15606936416184972\n", + "\n", "4,116 training samples\n", - " 458 validation samples\n" + "Ratio: 0.15294117647058825\n", + "\n", + " 458 validation samples\n", + "Ratio: 0.1683673469387755\n", + "\n" ] } ] @@ -4105,7 +4136,7 @@ "metadata": { "id": "k4pXght6hre3" }, - "execution_count": 10, + "execution_count": null, "outputs": [] }, { @@ -4137,7 +4168,7 @@ "id": "aUvyBFxzmBUy", "outputId": "04bc746e-0d7a-443f-dfd2-df757b49cc04" }, - "execution_count": 11, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -4187,7 +4218,7 @@ "id": "sIP3VGZmiK9s", "outputId": "ebac6f6b-b0c4-49a3-8da7-2c98fe1bbbf1" }, - "execution_count": 12, + "execution_count": null, "outputs": [ { "output_type": "display_data", @@ -4228,7 +4259,7 @@ "id": "LhpRR5YEeU1S", "outputId": "c1e1d515-12fe-41bf-94b5-c61ad83afbc2" }, - "execution_count": 13, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -4471,7 +4502,7 @@ "metadata": { "id": "_uffUPNEi3S5" }, - "execution_count": 14, + "execution_count": null, "outputs": [] }, { @@ -4501,7 +4532,7 @@ "metadata": { "id": "Z3XSZuFmkgVr" }, - "execution_count": 15, + "execution_count": null, "outputs": [] }, { @@ -4642,7 +4673,7 @@ "id": "hnq-2iztdYie", "outputId": "f2cf6703-9ab8-4dbf-e5d4-22f899a28776" }, - "execution_count": 16, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -4775,7 +4806,7 @@ "id": "qVSGSZ5-gbnV", "outputId": "b6e5d689-6748-4e0d-a43d-0484de05129d" }, - "execution_count": 17, + "execution_count": null, "outputs": [ { "output_type": "execute_result", @@ -4976,7 +5007,7 @@ "id": "qhDALEePhHp1", "outputId": "ddcab2bb-da52-4647-8d04-167993f6c98f" }, - "execution_count": 18, + "execution_count": null, "outputs": [ { "output_type": "display_data", @@ -5011,7 +5042,7 @@ "metadata": { "id": "du6qCdHyhMms" }, - "execution_count": 19, + "execution_count": null, "outputs": [] }, { @@ -5057,7 +5088,7 @@ "id": "s3nFSXgbhRs1", "outputId": "39a16e42-8d7e-4e31-95f1-e29118ce62f3" }, - "execution_count": 20, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -5091,7 +5122,7 @@ "id": "eNMYIt7RhWYM", "outputId": "7257f066-6539-4e42-d0ae-e6c5609f1812" }, - "execution_count": 21, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -5132,7 +5163,7 @@ "id": "oqfHjUXThb2J", "outputId": "2bbfcaeb-5ea8-498e-a5a0-8f2fac83feea" }, - "execution_count": 22, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -5162,7 +5193,7 @@ "id": "JJoRzvr0hePf", "outputId": "ebc78102-65e6-4847-d3c0-d3825870dc78" }, - "execution_count": 23, + "execution_count": null, "outputs": [ { "output_type": "display_data", @@ -5195,7 +5226,7 @@ "id": "8XER3sOFhfny", "outputId": "77ec6114-8ab3-4abd-c7b7-de95528a2bef" }, - "execution_count": 24, + "execution_count": null, "outputs": [ { "output_type": "stream", @@ -5236,7 +5267,7 @@ "id": "gqSUWqCqhizx", "outputId": "76d1febd-031d-456a-b108-7b664b2b5729" }, - "execution_count": 25, + "execution_count": null, "outputs": [ { "output_type": "stream", diff --git a/projekt/README.md b/projekt/README.md index ebbe8cf..c558cba 100644 --- a/projekt/README.md +++ b/projekt/README.md @@ -62,6 +62,20 @@ Najważniejsze cechy: - Accuracy: 99% - MCC: 0.960 +### Transformer Decoder - GPT2 - modyfikacja +Najważniejsze cechy: +- wytrenowany model gpt2 +- typ modelu zmodyfikowany transformers.GPT2ForSequenceClassification - dodatkowe dwie warstwy liniowe +- input modelu - treść smsa +- output modelu - klasa 1 lub 2 +- finetuning na zbiorze treningowym + - adamW optimizer + - learning rate 2e-5 + - 8 batch size + - 4 epoch +- Accuracy: 99.1% +- MCC: 0.958 + ### Transformer Encoder-Decoder - T5 Najważniejsze cechy: - wytrenowany model t5-base diff --git a/projekt/T5_sms_spam.ipynb b/projekt/T5_sms_spam.ipynb index 50c41db..d39fbaf 100644 --- a/projekt/T5_sms_spam.ipynb +++ b/projekt/T5_sms_spam.ipynb @@ -17,7 +17,7 @@ "base_uri": "https://localhost:8080/" }, "id": "8l0hzptKNiZS", - "outputId": "29622435-2f50-4c0a-d921-e0bee5470440" + "outputId": "00b4e80b-9d2a-42f1-e087-1412429b63bd" }, "outputs": [ { @@ -27,55 +27,55 @@ "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[31m47.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m39.5 MB/s\u001b[0m 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(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", @@ -84,7 +84,7 @@ " 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 sentencepiece-0.1.97 tokenizers-0.13.2 transformers-4.26.1 urllib3-1.26.14 xxhash-3.2.0\n" + "Successfully installed datasets-2.9.0 huggingface-hub-0.12.1 multiprocess-0.70.14 responses-0.18.0 sentencepiece-0.1.97 tokenizers-0.13.2 transformers-4.26.1 urllib3-1.26.14 xxhash-3.2.0\n" ] } ], @@ -118,78 +118,78 @@ "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 231, + "height": 263, "referenced_widgets": [ - "d481c2d945214c45b716d266d5d75184", - "b59689c435074e14a5ffab86f7358d79", - "8e53b6fe3c9b4ddb95c6bc13431f330e", - "a8e97add56944729a2ead40f46e0acd8", - 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"outputId": "f0e3ddd0-5cc7-47e2-9910-8b6b84cbd896" }, "outputs": [ { @@ -201,7 +201,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "d481c2d945214c45b716d266d5d75184" + "model_id": "c396a3f65bb947ffa33130c424d9d93b" } }, "metadata": {} @@ -215,7 +215,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "0a4967eb8656482988be61b4e884ab6b" + "model_id": "2c5a4622661a4465910b5f1f95bea742" } }, "metadata": {} @@ -229,7 +229,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "36974fdfee5141b7850289e25b23df3c" + "model_id": "d64108ba247a4ac5a93b3bdeede7fd9a" } }, "metadata": {} @@ -250,7 +250,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "4bbbc1eaabb0460cba315a082cd69783" + "model_id": "b8c9fbbce7b84bf4a2f5b90c9d35ce0f" } }, "metadata": {} @@ -264,7 +264,7 @@ 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}, "metadata": {} @@ -449,7 +449,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "bf509a7ff72a4e40bfea72feb958477c" + "model_id": "e76d348dd73d4e88994fa53449b69a0c" } }, "metadata": {} @@ -479,7 +479,7 @@ "base_uri": "https://localhost:8080/" }, "id": "dfxJQpoePsvI", - "outputId": "305252ef-73f2-4677-d7c9-31efc4d249a6" + "outputId": "a4b4cfa8-5334-4be6-c3ec-124840ecdcfa" }, "outputs": [ { @@ -516,7 +516,7 @@ "base_uri": "https://localhost:8080/" }, "id": "7uNUkixPU85O", - "outputId": "6812abc8-d279-4ee8-9947-9c9a5932e47c" + "outputId": "b34a2f27-9478-4fc9-cdbb-23081472ec92" }, "outputs": [ { @@ -545,7 +545,7 @@ "base_uri": "https://localhost:8080/" }, "id": "lj0issBznZfK", - "outputId": "9dccb0f2-6452-460b-b955-83468285a635" + "outputId": "d406d5a6-e278-47aa-b03e-8ee33c5871ac" }, "outputs": [ { @@ -594,7 +594,7 @@ "base_uri": "https://localhost:8080/" }, "id": "Z28QYfLnSGxR", - "outputId": "bcb84fca-c7e0-4e4a-a8f6-ff3db86e143d" + "outputId": "aa3c2dce-488c-48a5-f47d-18026ac678d6" }, "outputs": [ { @@ -680,18 +680,42 @@ "print('Label token IDs:', target_ids[0])" ] }, + { + "cell_type": "code", + "source": [ + "print('Label token IDs:', target_ids[123])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Ld1xH-BD0G-M", + "outputId": "67aca9e1-5dca-48d7-97b7-26f9781bf51f" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Label token IDs: tensor([209, 1, 0])\n" + ] + } + ] + }, { "cell_type": "markdown", "metadata": { "id": "qD_t0y0KVVSy" }, "source": [ - "# Split dataset" + "# Split dataset\n", + "Class balance ratio should be similar to base dataset ratio." ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": { "id": "vN_SatRIVa4c" }, @@ -702,27 +726,55 @@ }, { "cell_type": "code", - "execution_count": 14, + "source": [ + "def check_class_balance(dataset):\n", + " spam_count = 0.0\n", + " not_spam_count = 0.0\n", + " for row in dataset:\n", + " if row[2][1].item() == 1:\n", + " spam_count += 1.0\n", + " else:\n", + " not_spam_count += 1.0\n", + " return spam_count / not_spam_count " + ], + "metadata": { + "id": "oo9C8ATt0dTq" + }, + "execution_count": 15, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Mm6vc6lLVW3l", - "outputId": "2ff9533f-7117-4492-dae2-5a8dada86e41" + "outputId": "e7223b64-86a7-459d-b681-1ea1e0db02d8" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ + "Spam to not spam messages ratio: 0.15475450590428838\n", + "\n", "1,000 test samples\n", + "Ratio: 0.15074798619102417\n", + "\n", "4,116 training samples\n", - " 458 validation samples\n" + "Ratio: 0.15455820476858345\n", + "\n", + " 458 validation samples\n", + "Ratio: 0.16539440203562342\n", + "\n" ] } ], "source": [ "dataset = TensorDataset(input_ids, attention_masks, target_ids)\n", + "print(\"Spam to not spam messages ratio: {}\\n\".format(check_class_balance(dataset)))\n", "\n", "test_size = 1000\n", "dataset_len = len(dataset)\n", @@ -732,8 +784,11 @@ "test_dataset, train_dataset, val_dataset = random_split(dataset, [test_size, train_size, val_size])\n", "\n", "print('{:>5,} test samples'.format(test_size))\n", + "print(\"Ratio: {}\\n\".format(check_class_balance(test_dataset)))\n", "print('{:>5,} training samples'.format(train_size))\n", - "print('{:>5,} validation samples'.format(val_size))" + "print(\"Ratio: {}\\n\".format(check_class_balance(train_dataset)))\n", + "print('{:>5,} validation samples'.format(val_size))\n", + "print(\"Ratio: {}\\n\".format(check_class_balance(val_dataset)))" ] }, { @@ -747,7 +802,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { "id": "CxnQ3cmIVlNh" }, @@ -758,7 +813,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { "id": "0hcpO_onVjEC" }, @@ -790,7 +845,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -831,7 +886,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "id": "Eu-7Eed8WgN0" }, @@ -842,7 +897,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1647,7 +1702,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { "id": "s-q6_F38bLVA" }, @@ -1659,7 +1714,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "id": "FzUi8908ax61" }, @@ -1696,7 +1751,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "id": "A7XUF4PNbYy8" }, @@ -1722,7 +1777,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, 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