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python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n", "Installing collected packages: tokenizers, sentencepiece, xxhash, urllib3, multiprocess, responses, huggingface-hub, 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 sentencepiece-0.1.97 tokenizers-0.13.2 transformers-4.26.1 urllib3-1.26.14 xxhash-3.2.0\n" ] } ], "source": [ "!pip install transformers datasets torch sentencepiece" ] }, { "cell_type": "markdown", "source": [ "# Załadowanie datasetu" ], "metadata": { "id": "dhN0rmb5Oi3d" } }, { "cell_type": "code", "source": [ "from datasets import load_dataset" ], "metadata": { "id": "tnaDkwZ2Pbnn" }, "execution_count": 2, "outputs": [] }, { "cell_type": "code", "source": [ "dataset = load_dataset(\"sms_spam\")" ], "metadata": { "colab": { 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"498224e7a7b54c56ba45261a1b39c1c3", "e9319122f48e43a5b15ef55823347507", "7ea7ab8cba484142b5a3d8019e9c9c84", "fddd4ee4bc054b0f90ed88018fc3e3a0", "55b5df3163a34561a4a5ba27efada434", "2823c1041e914dd8887498410baaab43", "fba57e030f624bbab5b51b65d7d36722", "86fa9ce6cd9b4ec9af6d24d943aac75b", "97a0725bbb044103911e1e29bb07360e", "1cf99cee3c374e30abd95c03e9696bbe", "d17a7d096d964a41a7ee183f5028c037", "c6a51ab4f16649c0899bbfd14fdc9cb8", "876c522f96a64809a82d316f4afa1bae", "1c38d46a00224aa791cad25fdd4d33e2" ] }, "id": "cCiAuRqrOkvV", "outputId": "b8dfca85-8b7a-4321-da77-ec7eea1843e9" }, "execution_count": 3, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading builder script: 0%| | 0.00/3.21k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "137fa30b14f34f57a0beb8a6c6e60cf5" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading metadata: 0%| | 0.00/1.69k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "47463520a286401ba3d5d29fce07ede5" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading readme: 0%| | 0.00/4.87k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "0926a24353a94b82a8e405ec72bef775" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Downloading and preparing dataset sms_spam/plain_text to /root/.cache/huggingface/datasets/sms_spam/plain_text/1.0.0/53f051d3b5f62d99d61792c91acefe4f1577ad3e4c216fb0ad39e30b9f20019c...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading data: 0%| | 0.00/203k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "320ba3e1e74541288c307eedbd5e2754" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Generating train split: 0%| | 0/5574 [00:00, ? examples/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "a950c7427a174b22abdd17fb7710ece7" } }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Dataset sms_spam downloaded and prepared to /root/.cache/huggingface/datasets/sms_spam/plain_text/1.0.0/53f051d3b5f62d99d61792c91acefe4f1577ad3e4c216fb0ad39e30b9f20019c. Subsequent calls will reuse this data.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ " 0%| | 0/1 [00:00, ?it/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "fddd4ee4bc054b0f90ed88018fc3e3a0" } }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "dataset['train'][0]" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JKFHPko3OnAV", "outputId": "2048bc4f-4d5f-45e4-e5c9-0be61d9d7349" }, "execution_count": 4, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'sms': 'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\\n',\n", " 'label': 0}" ] }, "metadata": {}, "execution_count": 4 } ] }, { "cell_type": "markdown", "source": [ "# Modyfikacja datasetu - klasyfikacja" ], "metadata": { "id": "l140vJrgYxPr" } }, { "cell_type": "code", "source": [ "parsed_dataset = []\n", "\n", "for row in dataset['train']:\n", " text = \"binary classification: \" + row['sms'].replace(\"\\n\", \"\")\n", " new_row = {}\n", " new_row['sms'] = text\n", " if row['label'] == 0:\n", " new_row['label'] = \"0\"\n", " else:\n", " new_row['label'] = \"1\"\n", " parsed_dataset.append(new_row)\n", "\n", "parsed_dataset[0]" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1boUF-YiY3_y", "outputId": "fed6fa9c-8699-4727-ae1b-37475f831b61" }, "execution_count": 5, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'sms': 'binary classification: Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...',\n", " 'label': '0'}" ] }, "metadata": {}, "execution_count": 5 } ] }, { "cell_type": "markdown", "source": [ "# Tokenizer T5" ], "metadata": { "id": "O-J-jBDxPJcn" } }, { "cell_type": "code", "source": [ "from transformers import T5Tokenizer" ], "metadata": { "id": "P23AYPX1PZ6g" }, "execution_count": 6, "outputs": [] }, { "cell_type": "code", "source": [ "tokenizer = T5Tokenizer.from_pretrained('t5-base')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 203, "referenced_widgets": [ "6e2b903343ad49c89339a38a1c626619", "b1e96f5c00d048c69c1c0fadeb31dcd9", "ec994f6daafb4ef8a08371f2394918dd", "699d9f4479854372ada35ab38fe80352", "cb36ca390eac4c76ad8cfbcbdb5b6950", "5ffe2a0b8e3342a9b764fbbdf1395f1c", "ea92e7a968d4479e842c368ece4b60c1", "95a4496f03414cf8a8d7cf5e6cc3f37b", "7b1d15df592048fe8a8c043e7a8461ad", "cc73442788a3462a8d5d53c9c799df7a", "ee75218b2c4047fdb265df7a54feea78", "e720c7e5ef0849918eb6e7123673c95e", "8b2ff14cab9941388b547140d06e1dd5", "0eb67bf20ecf4cc0b5f3bd0589440e6b", "4e976d7959e640f4b098d9a02320f228", "48de3465dc194fff9903fd3813aae91a", "29558f7cc7574024879d274548ac4cd7", "ce5531904574465d84d65365b0fc2951", "10cc03c556cf4fb791697277b3deef35", "2113acabfb014e7ab55d099d28845914", "504b320daae543b78b8777cebbe65dea", "5ea6f5fa80184fe58ef86a536ec0f8f0" ] }, "id": "q5Jz0E_oPMBr", "outputId": "bbe8a564-fee5-42ef-da4d-04ae4af111db" }, "execution_count": 7, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading (…)ve/main/spiece.model: 0%| | 0.00/792k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "6e2b903343ad49c89339a38a1c626619" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading (…)lve/main/config.json: 0%| | 0.00/1.21k [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "e720c7e5ef0849918eb6e7123673c95e" } }, "metadata": {} }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.8/dist-packages/transformers/models/t5/tokenization_t5.py:163: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n", "For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n", "- Be aware that you SHOULD NOT rely on t5-base automatically truncating your input to 512 when padding/encoding.\n", "- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n", "- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n", " warnings.warn(\n" ] } ] }, { "cell_type": "code", "source": [ "sms = parsed_dataset[0]['sms']\n", "print('Original: ', sms)\n", "print('Tokenized: ', tokenizer.tokenize(sms))\n", "print('Token IDs: ', tokenizer.convert_tokens_to_ids(tokenizer.tokenize(sms)))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dfxJQpoePsvI", "outputId": "fa44a9cd-aff1-4b64-957e-52595dad7472" }, "execution_count": 8, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Original: binary classification: Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\n", "Tokenized: ['▁binary', '▁classification', ':', '▁Go', '▁until', '▁jur', 'ong', '▁point', ',', '▁crazy', '.', '.', '▁Available', '▁only', '▁in', '▁bug', 'is', '▁', 'n', '▁great', '▁world', '▁la', '▁', 'e', '▁buffet', '...', '▁Cine', '▁there', '▁got', '▁', 'a', 'more', '▁wa', 't', '...']\n", "Token IDs: [14865, 13774, 10, 1263, 552, 10081, 2444, 500, 6, 6139, 5, 5, 8144, 163, 16, 8143, 159, 3, 29, 248, 296, 50, 3, 15, 15385, 233, 17270, 132, 530, 3, 9, 3706, 8036, 17, 233]\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Check maximum lenght of a sentence" ], "metadata": { "id": "UpluhM8cU5Ir" } }, { "cell_type": "code", "source": [ "max_len = 0\n", "\n", "for sentence in parsed_dataset:\n", " input_ids = tokenizer.encode(sentence['sms'], add_special_tokens=True)\n", " max_len = max(max_len, len(input_ids))\n", "\n", "print('Max sentence length: ', max_len)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7uNUkixPU85O", "outputId": "2ec78c60-f5ae-4201-c8e5-30208c94efab" }, "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Max sentence length: 341\n" ] } ] }, { "cell_type": "code", "source": [ "max_label_len = 0\n", "\n", "for sentence in parsed_dataset:\n", " input_ids = tokenizer.encode(sentence['label'], add_special_tokens=True)\n", " max_label_len = max(max_label_len, len(input_ids))\n", "\n", "print('Max sentence length: ', max_label_len)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "lj0issBznZfK", "outputId": "2fb86f95-c0a0-45ea-a36d-a6b174f32aac" }, "execution_count": 10, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Max sentence length: 3\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Pre train tokenization" ], "metadata": { "id": "nfw62HdgSERb" } }, { "cell_type": "code", "source": [ "import torch" ], "metadata": { "id": "KTXYalS1VLqH" }, "execution_count": 11, "outputs": [] }, { "cell_type": "code", "source": [ "input_ids = []\n", "target_ids = []\n", "attention_masks = []\n", "\n", "for sentence in parsed_dataset:\n", " encoded_dict = tokenizer.encode_plus(\n", " sentence['sms'],\n", " add_special_tokens = True,\n", " max_length = 341,\n", " padding = 'max_length',\n", " truncation=True,\n", " return_attention_mask = True,\n", " return_tensors = 'pt',\n", " )\n", " \n", " encoded_target_dict = tokenizer.encode_plus(\n", " sentence['label'],\n", " add_special_tokens = True,\n", " max_length = 3,\n", " padding = 'max_length',\n", " truncation=True,\n", " return_attention_mask = True,\n", " return_tensors = 'pt',\n", " )\n", " \n", " input_ids.append(encoded_dict['input_ids'])\n", " target_ids.append(encoded_target_dict['input_ids'])\n", " attention_masks.append(encoded_dict['attention_mask'])\n", "\n", "input_ids = torch.cat(input_ids, dim=0)\n", "target_ids = torch.cat(target_ids, dim=0)\n", "attention_masks = torch.cat(attention_masks, dim=0)\n", "\n", "print('Original: ', parsed_dataset[0])\n", "print('Token IDs:', input_ids[0])\n", "print('Label token IDs:', target_ids[0])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Z28QYfLnSGxR", "outputId": "e90e2369-25d1-4fc1-b7fd-b805eaf1f5de" }, "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Original: {'sms': 'binary classification: Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...', 'label': '0'}\n", "Token IDs: tensor([14865, 13774, 10, 1263, 552, 10081, 2444, 500, 6, 6139,\n", " 5, 5, 8144, 163, 16, 8143, 159, 3, 29, 248,\n", " 296, 50, 3, 15, 15385, 233, 17270, 132, 530, 3,\n", " 9, 3706, 8036, 17, 233, 1, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", " 0])\n", "Label token IDs: tensor([ 3, 632, 1])\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Split dataset" ], "metadata": { "id": "qD_t0y0KVVSy" } }, { "cell_type": "code", "source": [ "from torch.utils.data import TensorDataset, random_split" ], "metadata": { "id": "vN_SatRIVa4c" }, "execution_count": 13, "outputs": [] }, { "cell_type": "code", "source": [ "dataset = TensorDataset(input_ids, attention_masks, target_ids)\n", "\n", "test_size = 1000\n", "dataset_len = len(dataset)\n", "train_size = int(0.9 * (dataset_len-test_size))\n", "val_size = (dataset_len-test_size) - train_size\n", "\n", "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('{:>5,} training samples'.format(train_size))\n", "print('{:>5,} validation samples'.format(val_size))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Mm6vc6lLVW3l", "outputId": "af8a7007-791f-426c-c277-1c77a1fd9d78" }, "execution_count": 14, "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": "bmgQOP4EVfA1" } }, { "cell_type": "code", "source": [ "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler" ], "metadata": { "id": "CxnQ3cmIVlNh" }, "execution_count": 15, "outputs": [] }, { "cell_type": "code", "source": [ "batch_size = 16\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": "0hcpO_onVjEC" }, "execution_count": 16, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Device check" ], "metadata": { "id": "efwhqLyyVu9z" } }, { "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": "ANBCfNGnVwVk", "outputId": "02086b95-30b8-4be0-aa4b-ac0041b4b007" }, "execution_count": 17, "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 T5 model" ], "metadata": { "id": "okTx_ynMV0rH" } }, { "cell_type": "code", "source": [ "from transformers import T5ForConditionalGeneration" ], "metadata": { "id": "Eu-7Eed8WgN0" }, "execution_count": 18, "outputs": [] }, { "cell_type": "code", "source": [ "model = T5ForConditionalGeneration.from_pretrained('t5-base')\n", "\n", "model.cuda()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "b1d7a5cf900b48408e515baa4c66a1cd", "6846a2acd95b45a3a0e2cb79f552f0c0", "fce901d34cc34feeb92854999e98c0f9", "5a89ce40643247fda326742531912a01", "bcc7a0cd035e485680b41e7c4a78b8f8", "bd0a578fefb44fb4b2662d59fd2ff12e", "6585bd6115c047fd881c0bfd323142f0", "dbc7f7aa90174ff68b5cc829a6fd8690", "ca3a8e4611c6422380351b947882876a", "2470365762844b62a09dc6fa818c4a09", "3f2489ce0ae941a1a720c60a3052ee70", "0795a8385c68409fb5539b9ea6756a47", "05dfc6dc9f78483da34b2c6513315e7d", "5cfe28a638cb42fc914dc81eb02a46f4", "d061dcb2f3e840ec9ba6a6ec4d972619", "df418dee3efd4da8aa57ca0044190b2e", "9d3d394c756d4eabb0f3fd66ba8ef05a", "00612595fa42467a83aa6e4b55343339", "33521be9887b4c368915b4f8f2438440", "990a862f07894fa9b9f08d3bb7e082ca", "1b793ae9c46740bdbbec5e617a899683", "cbfde7f5f0204417abdced523c5621e9" ] }, "id": "JKv9O8kfV2zZ", "outputId": "b4b823d6-f7dc-4b78-a12b-4a2bae4e463f" }, "execution_count": 19, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading (…)\"pytorch_model.bin\";: 0%| | 0.00/892M [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "b1d7a5cf900b48408e515baa4c66a1cd" } }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading (…)neration_config.json: 0%| | 0.00/147 [00:00, ?B/s]" ], "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, "model_id": "0795a8385c68409fb5539b9ea6756a47" } }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "T5ForConditionalGeneration(\n", " (shared): Embedding(32128, 768)\n", " (encoder): T5Stack(\n", " (embed_tokens): Embedding(32128, 768)\n", " (block): ModuleList(\n", " (0): T5Block(\n", " (layer): ModuleList(\n", " (0): T5LayerSelfAttention(\n", " (SelfAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " (relative_attention_bias): Embedding(32, 12)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (1): T5LayerFF(\n", " (DenseReluDense): T5DenseActDense(\n", " (wi): Linear(in_features=768, out_features=3072, bias=False)\n", " (wo): Linear(in_features=3072, out_features=768, bias=False)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (act): ReLU()\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " )\n", " (1): T5Block(\n", " (layer): ModuleList(\n", " (0): T5LayerSelfAttention(\n", " (SelfAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (1): T5LayerFF(\n", " (DenseReluDense): T5DenseActDense(\n", " (wi): Linear(in_features=768, out_features=3072, bias=False)\n", " (wo): Linear(in_features=3072, out_features=768, bias=False)\n", " (dropout): 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(q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (2): T5LayerFF(\n", " (DenseReluDense): T5DenseActDense(\n", " (wi): Linear(in_features=768, out_features=3072, bias=False)\n", " (wo): Linear(in_features=3072, out_features=768, bias=False)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (act): ReLU()\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " )\n", " (6): T5Block(\n", " (layer): ModuleList(\n", " (0): T5LayerSelfAttention(\n", " (SelfAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (1): T5LayerCrossAttention(\n", " (EncDecAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (2): T5LayerFF(\n", " (DenseReluDense): T5DenseActDense(\n", " (wi): Linear(in_features=768, out_features=3072, bias=False)\n", " (wo): Linear(in_features=3072, out_features=768, bias=False)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (act): ReLU()\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " )\n", " (7): T5Block(\n", " (layer): ModuleList(\n", " (0): T5LayerSelfAttention(\n", " (SelfAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (1): T5LayerCrossAttention(\n", " (EncDecAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (2): T5LayerFF(\n", " (DenseReluDense): T5DenseActDense(\n", " (wi): Linear(in_features=768, out_features=3072, bias=False)\n", " (wo): Linear(in_features=3072, out_features=768, bias=False)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (act): ReLU()\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " )\n", " (8): T5Block(\n", " (layer): ModuleList(\n", " (0): T5LayerSelfAttention(\n", " (SelfAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (1): T5LayerCrossAttention(\n", " (EncDecAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (2): T5LayerFF(\n", " (DenseReluDense): T5DenseActDense(\n", " (wi): Linear(in_features=768, out_features=3072, bias=False)\n", " (wo): Linear(in_features=3072, out_features=768, bias=False)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (act): ReLU()\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " )\n", " (9): T5Block(\n", " (layer): ModuleList(\n", " (0): T5LayerSelfAttention(\n", " (SelfAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (1): T5LayerCrossAttention(\n", " (EncDecAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (2): T5LayerFF(\n", " (DenseReluDense): T5DenseActDense(\n", " (wi): Linear(in_features=768, out_features=3072, bias=False)\n", " (wo): Linear(in_features=3072, out_features=768, bias=False)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (act): ReLU()\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " )\n", " (10): T5Block(\n", " (layer): ModuleList(\n", " (0): T5LayerSelfAttention(\n", " (SelfAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (1): T5LayerCrossAttention(\n", " (EncDecAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (2): T5LayerFF(\n", " (DenseReluDense): T5DenseActDense(\n", " (wi): Linear(in_features=768, out_features=3072, bias=False)\n", " (wo): Linear(in_features=3072, out_features=768, bias=False)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (act): ReLU()\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " )\n", " (11): T5Block(\n", " (layer): ModuleList(\n", " (0): T5LayerSelfAttention(\n", " (SelfAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (1): T5LayerCrossAttention(\n", " (EncDecAttention): T5Attention(\n", " (q): Linear(in_features=768, out_features=768, bias=False)\n", " (k): Linear(in_features=768, out_features=768, bias=False)\n", " (v): Linear(in_features=768, out_features=768, bias=False)\n", " (o): Linear(in_features=768, out_features=768, bias=False)\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (2): T5LayerFF(\n", " (DenseReluDense): T5DenseActDense(\n", " (wi): Linear(in_features=768, out_features=3072, bias=False)\n", " (wo): Linear(in_features=3072, out_features=768, bias=False)\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " (act): ReLU()\n", " )\n", " (layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " )\n", " )\n", " )\n", " (final_layer_norm): T5LayerNorm()\n", " (dropout): Dropout(p=0.1, inplace=False)\n", " )\n", " (lm_head): Linear(in_features=768, out_features=32128, bias=False)\n", ")" ] }, "metadata": {}, "execution_count": 19 } ] }, { "cell_type": "markdown", "source": [ "# Helper functions" ], "metadata": { "id": "F_SDAwxoawDy" } }, { "cell_type": "code", "source": [ "import datetime\n", "import numpy as np" ], "metadata": { "id": "s-q6_F38bLVA" }, "execution_count": 20, "outputs": [] }, { "cell_type": "code", "source": [ "def calculate_accuracy(preds, target):\n", " results_ok = 0.0\n", " results_false = 0.0\n", "\n", " for idx, pred in enumerate(preds):\n", " if pred == target[idx]:\n", " results_ok += 1.0\n", " else:\n", " results_false += 1.0\n", "\n", " return results_ok / (results_ok + results_false)\n", "\n", "def format_time(elapsed):\n", " '''\n", " Takes a time in seconds and returns a string hh:mm:ss\n", " '''\n", " elapsed_rounded = int(round((elapsed)))\n", " return str(datetime.timedelta(seconds=elapsed_rounded))" ], "metadata": { "id": "FzUi8908ax61" }, "execution_count": 21, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Init training" ], "metadata": { "id": "ucChBa-9bXJy" } }, { "cell_type": "code", "source": [ "from transformers import get_linear_schedule_with_warmup" ], "metadata": { "id": "c9e7rbGwbdEp" }, "execution_count": 22, "outputs": [] }, { "cell_type": "code", "source": [ "optimizer = torch.optim.AdamW(model.parameters(),\n", " lr = 3e-4,\n", " eps = 1e-8\n", " )\n", "\n", "epochs = 4\n", "\n", "total_steps = len(train_dataloader) * epochs\n", "\n", "scheduler = get_linear_schedule_with_warmup(optimizer, \n", " num_warmup_steps = 0,\n", " num_training_steps = total_steps)" ], "metadata": { "id": "A7XUF4PNbYy8" }, "execution_count": 23, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Training" ], "metadata": { "id": "DAzQWODja0A3" } }, { "cell_type": "code", "source": [ "import random\n", "import time" ], "metadata": { "id": "Hoa7NlU0bI7G" }, "execution_count": 24, "outputs": [] }, { "cell_type": "code", "source": [ "# This training code is based on the `run_glue.py` script here:\n", "# https://github.com/huggingface/transformers/blob/5bfcd0485ece086ebcbed2d008813037968a9e58/examples/run_glue.py#L128\n", "\n", "seed_val = 42\n", "\n", "random.seed(seed_val)\n", "np.random.seed(seed_val)\n", "torch.manual_seed(seed_val)\n", "torch.cuda.manual_seed_all(seed_val)\n", "\n", "training_stats = []\n", "total_t0 = time.time()\n", "\n", "for epoch_i in range(0, epochs):\n", " \n", " # ========================================\n", " # Training\n", " # ========================================\n", "\n", " print(\"\")\n", " print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))\n", " print('Training...')\n", "\n", " t0 = time.time()\n", " total_train_loss = 0\n", " total_train_acc = 0\n", "\n", " model.train()\n", "\n", " for step, batch in enumerate(train_dataloader):\n", " if step % 40 == 0 and not step == 0:\n", " elapsed = format_time(time.time() - t0)\n", " print(' Batch {:>5,} 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", "\n", " y = batch[2].to(device)\n", " y_ids = y[:, :-1].contiguous()\n", " lm_labels = y[:, 1:].clone().detach()\n", " lm_labels[y[:, 1:] == tokenizer.pad_token_id] = -100\n", "\n", " model.zero_grad() \n", "\n", " outputs = model(\n", " input_ids=b_input_ids,\n", " attention_mask=b_input_mask,\n", " decoder_input_ids=y_ids,\n", " labels=lm_labels\n", " )\n", "\n", " generated_ids = model.generate(\n", " input_ids = b_input_ids,\n", " attention_mask = b_input_mask, \n", " max_length=2, \n", " num_beams=2,\n", " repetition_penalty=2.5, \n", " length_penalty=1.0, \n", " early_stopping=True\n", " )\n", "\n", " preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]\n", " target = [tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True) for t in y]\n", " total_train_acc += calculate_accuracy(preds, target) \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", " avg_train_acc = total_train_acc / 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(\" Average training acc: {0:.2f}\".format(avg_train_acc))\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_loss = 0\n", " total_eval_accuracy = 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", "\n", " y = batch[2].to(device)\n", " y_ids = y[:, :-1].contiguous()\n", " lm_labels = y[:, 1:].clone().detach()\n", " lm_labels[y[:, 1:] == tokenizer.pad_token_id] = -100\n", " \n", " with torch.no_grad(): \n", "\n", " outputs = model(\n", " input_ids=b_input_ids,\n", " attention_mask=b_input_mask,\n", " decoder_input_ids=y_ids,\n", " labels=lm_labels\n", " )\n", "\n", " loss = outputs['loss']\n", " total_eval_loss += loss.item()\n", "\n", " generated_ids = model.generate(\n", " input_ids = b_input_ids,\n", " attention_mask = b_input_mask, \n", " max_length=2, \n", " num_beams=2,\n", " repetition_penalty=2.5, \n", " length_penalty=1.0, \n", " early_stopping=True\n", " )\n", "\n", " preds = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True) for g in generated_ids]\n", " target = [tokenizer.decode(t, skip_special_tokens=True, clean_up_tokenization_spaces=True) for t in y]\n", " total_eval_accuracy += calculate_accuracy(preds, target) \n", "\n", " avg_val_loss = total_eval_loss / len(validation_dataloader)\n", "\n", " avg_val_accuracy = total_eval_accuracy / len(validation_dataloader)\n", " print(\" Accuracy: {0:.2f}\".format(avg_val_accuracy))\n", " \n", " validation_time = format_time(time.time() - t0)\n", " print(\" Validation took: {:}\".format(validation_time))\n", " print(\" Validation Loss: {0:.2f}\".format(avg_val_loss))\n", "\n", " training_stats.append(\n", " {\n", " 'epoch': epoch_i + 1,\n", " 'Training Loss': avg_train_loss,\n", " 'Training Accur.': avg_train_acc,\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": "xsHxfslka1u5", "outputId": "60bea81f-a963-4599-ca22-b1992c14a3e5" }, "execution_count": 25, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "======== Epoch 1 / 4 ========\n", "Training...\n", " Batch 40 of 258. Elapsed: 0:01:06.\n", " Batch 80 of 258. Elapsed: 0:02:13.\n", " Batch 120 of 258. Elapsed: 0:03:22.\n", " Batch 160 of 258. Elapsed: 0:04:32.\n", " Batch 200 of 258. Elapsed: 0:05:42.\n", " Batch 240 of 258. Elapsed: 0:06:52.\n", "\n", " Average training loss: 0.09\n", " Average training acc: 0.42\n", " Training epcoh took: 0:07:22\n", "\n", "Running Validation...\n", " Accuracy: 0.68\n", " Validation took: 0:00:25\n", " Validation Loss: 0.00\n", "\n", "======== Epoch 2 / 4 ========\n", "Training...\n", " Batch 40 of 258. Elapsed: 0:01:10.\n", " Batch 80 of 258. Elapsed: 0:02:19.\n", " Batch 120 of 258. Elapsed: 0:03:29.\n", " Batch 160 of 258. Elapsed: 0:04:39.\n", " Batch 200 of 258. Elapsed: 0:05:48.\n", " Batch 240 of 258. Elapsed: 0:06:58.\n", "\n", " Average training loss: 0.00\n", " Average training acc: 0.49\n", " Training epcoh took: 0:07:28\n", "\n", "Running Validation...\n", " Accuracy: 0.72\n", " Validation took: 0:00:25\n", " Validation Loss: 0.00\n", "\n", "======== Epoch 3 / 4 ========\n", "Training...\n", " Batch 40 of 258. Elapsed: 0:01:10.\n", " Batch 80 of 258. Elapsed: 0:02:19.\n", " Batch 120 of 258. Elapsed: 0:03:29.\n", " Batch 160 of 258. Elapsed: 0:04:39.\n", " Batch 200 of 258. Elapsed: 0:05:49.\n", " Batch 240 of 258. Elapsed: 0:06:58.\n", "\n", " Average training loss: 0.00\n", " Average training acc: 0.50\n", " Training epcoh took: 0:07:29\n", "\n", "Running Validation...\n", " Accuracy: 0.72\n", " Validation took: 0:00:25\n", " Validation Loss: 0.00\n", "\n", "======== Epoch 4 / 4 ========\n", "Training...\n", " Batch 40 of 258. Elapsed: 0:01:10.\n", " Batch 80 of 258. Elapsed: 0:02:19.\n", " Batch 120 of 258. Elapsed: 0:03:29.\n", " Batch 160 of 258. Elapsed: 0:04:39.\n", " Batch 200 of 258. Elapsed: 0:05:49.\n", " Batch 240 of 258. Elapsed: 0:06:58.\n", "\n", " Average training loss: 0.00\n", " Average training acc: 0.50\n", " Training epcoh took: 0:07:29\n", "\n", "Running Validation...\n", " Accuracy: 0.72\n", " Validation took: 0:00:25\n", " Validation Loss: 0.00\n", "\n", "Training complete!\n", "Total training took 0:31:29 (h:mm:ss)\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Train summary" ], "metadata": { "id": "xIpFPoRb91Or" } }, { "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": "GjYqBrrO93Oh", "outputId": "d5742682-1cb4-4910-ab30-9424671b31e4" }, "execution_count": 26, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Training Loss Training Accur. Valid. Loss Valid. Accur. \\\n", "epoch \n", "1 8.67e-02 0.42 1.46e-08 0.68 \n", "2 2.02e-06 0.49 2.65e-10 0.72 \n", "3 1.50e-06 0.50 0.00e+00 0.72 \n", "4 1.10e-06 0.50 0.00e+00 0.72 \n", "\n", " Training Time Validation Time \n", "epoch \n", "1 0:07:22 0:00:25 \n", "2 0:07:28 0:00:25 \n", "3 0:07:29 0:00:25 \n", "4 0:07:29 0:00:25 " ], "text/html": [ "\n", "
\n", " | Training Loss | \n", "Training Accur. | \n", "Valid. Loss | \n", "Valid. Accur. | \n", "Training Time | \n", "Validation Time | \n", "
---|---|---|---|---|---|---|
epoch | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
1 | \n", "8.67e-02 | \n", "0.42 | \n", "1.46e-08 | \n", "0.68 | \n", "0:07:22 | \n", "0:00:25 | \n", "
2 | \n", "2.02e-06 | \n", "0.49 | \n", "2.65e-10 | \n", "0.72 | \n", "0:07:28 | \n", "0:00:25 | \n", "
3 | \n", "1.50e-06 | \n", "0.50 | \n", "0.00e+00 | \n", "0.72 | \n", "0:07:29 | \n", "0:00:25 | \n", "
4 | \n", "1.10e-06 | \n", "0.50 | \n", "0.00e+00 | \n", "0.72 | \n", "0:07:29 | \n", "0:00:25 | \n", "