5415 lines
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Plaintext
5415 lines
280 KiB
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{
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"# Instalacja pakietów"
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],
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]
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],
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"source": [
|
|||
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|
"!pip install transformers datasets torch"
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{
|
|||
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"source": [
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"# Załadowanie pakietów"
|
|||
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|
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|
|||
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"metadata": {
|
|||
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|
"id": "s8cfdy_6ldCn"
|
|||
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}
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{
|
|||
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"cell_type": "code",
|
|||
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"source": [
|
|||
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|
"from datasets import load_dataset\n",
|
|||
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|
"from transformers import BertTokenizer\n",
|
|||
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|
"import torch\n",
|
|||
|
|
"from torch.utils.data import TensorDataset, random_split\n",
|
|||
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"from torch.utils.data import DataLoader, RandomSampler, SequentialSampler\n",
|
|||
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"from transformers import BertForSequenceClassification, BertConfig\n",
|
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"from transformers import get_linear_schedule_with_warmup\n",
|
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"import numpy as np\n",
|
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"import time\n",
|
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"import datetime\n",
|
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"import random"
|
|||
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|
],
|
|||
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"metadata": {
|
|||
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|
"id": "yLS_x9DIlgSs"
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},
|
|||
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"execution_count": 2,
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|||
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"outputs": []
|
|||
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},
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{
|
|||
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"cell_type": "markdown",
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|||
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"source": [
|
|||
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"# Załadowanie datasetu\n",
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"sms_spam"
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],
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"metadata": {
|
|||
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"id": "fPwDyJd5cdaE"
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}
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},
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{
|
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|
|||
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"source": [
|
|||
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"dataset = load_dataset(\"sms_spam\")"
|
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],
|
|||
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|
|||
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"colab": {
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|||
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"height": 244,
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"25980d4dc17b412c9e76a9efdf251759",
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"704713f8cceb4792bb986520698d0be5",
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"d57ed46f96fa4d51bac74fcc326700cc",
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"bab7298185af41188969a03e81489efc",
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"b0fba461ad5f4826a082787223335564",
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"e6ec15b30b8040d38098ea1fd383b77d",
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"0f57b73714264ca8b6b5a83e7d03a340",
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Downloading and preparing dataset sms_spam/plain_text to /root/.cache/huggingface/datasets/sms_spam/plain_text/1.0.0/53f051d3b5f62d99d61792c91acefe4f1577ad3e4c216fb0ad39e30b9f20019c...\n"
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{
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"output_type": "stream",
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"name": "stdout",
|
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|
"text": [
|
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|
|
"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"
|
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{
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]
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},
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|
{
|
|||
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|
"cell_type": "code",
|
|||
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|
"source": [
|
|||
|
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"dataset['train'][0]"
|
|||
|
|
],
|
|||
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"metadata": {
|
|||
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|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
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},
|
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|
"id": "Mf1QIM_dlp2x",
|
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"outputId": "48b3eb91-1044-4fc2-ccc6-0187b8b8ca1f"
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},
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"execution_count": 4,
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"{'sms': 'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\\n',\n",
|
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" 'label': 0}"
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]
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},
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"metadata": {},
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"execution_count": 4
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}
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Tokenizer BERT"
|
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],
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"metadata": {
|
|||
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|
"id": "Qc7CIjSOchir"
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}
|
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|
},
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{
|
|||
|
|
"cell_type": "code",
|
|||
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|
"source": [
|
|||
|
|
"tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
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|
"height": 113,
|
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|
"referenced_widgets": [
|
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|
"645d79ef5a2143af93b9dd7a66398ef4",
|
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|
"3a5a82fb677b49fa964e7841a2a384c5",
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"c4e7374b8fdf420bb7024c820aac501d",
|
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|
"9e9b5577000f43d0a8c14ef9e17f2770",
|
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|
"a73714a6ba874de28edc7a6b61db1289",
|
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"5b745905ea6d4f08b51dbaaa05dba69c",
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"c83ab170d2704a1f8e32f70f6b40006b",
|
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"38ace4c2fd31432e801fd5f073c40a4f",
|
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|
"9de94d9afbf6465a9f23898d1789da67",
|
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"0c1e159cc977492fb1ff554818f8b90c",
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"b78fb9ef1ff9445d86a68eb37d7f6959",
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"4e79563a33e345a28ebd242f88f1391d",
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"b0e31c0236d043099a916902a79baa3b",
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"0d50f317048c4b3b9d0e433fc2b8b81f",
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"a74a190e28764f86980748e1861d4ae9",
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"d3e4e3c42fec41afa141c27b1f0ed279",
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"bf117c3c64c04ee6b94e03e82408ed2e",
|
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"b3ec2fe604104083986a7720d5a2dd65",
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"2247bfff362241e7bf036cf2596aea99",
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"07383fac57cf498e9eed23259a2d5763",
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"30e3e89595d541be81a95b2d600e4fba",
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"65f61b687a2041bbbb0f6807d2f5b01c",
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"71f3be8385a74b10a015974e168e6bd1",
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"f1b905ed8a0a412caab7515ea016267f",
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"a7fa5557847f453b89ded8dde3c372db",
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"a81e4849602f473386cb1782fa4de83d",
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"026b251acf294bb8902aff109a7f6f5d",
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"6a4966cc6cc841fe86a704cd98bdb52d",
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"371e82101aa14e90839490638c90a68e",
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"8dd3f484a6e94b8aae8b7cb1675e7ec1",
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"72a3ecffa85948169f786ab1154b943f",
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"79621956769e4912bb6965df4e2c574a",
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"e9a97237031d4bbd9a3582936f25d72b"
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]
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},
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|||
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|
"id": "hmnlC_hubLmP",
|
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{
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|
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|||
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|
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|
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{
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|
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}
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]
|
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|
},
|
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{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"sms = dataset['train'][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": "ZxigrpcQdWCF",
|
|||
|
|
"outputId": "6cea310c-9209-4c1b-be4d-e85e8397d2ee"
|
|||
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|
},
|
|||
|
|
"execution_count": 6,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Original: Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\n",
|
|||
|
|
"\n",
|
|||
|
|
"Tokenized: ['go', 'until', 'ju', '##rong', 'point', ',', 'crazy', '.', '.', 'available', 'only', 'in', 'bug', '##is', 'n', 'great', 'world', 'la', 'e', 'buffet', '.', '.', '.', 'ci', '##ne', 'there', 'got', 'amore', 'wat', '.', '.', '.']\n",
|
|||
|
|
"Token IDs: [2175, 2127, 18414, 17583, 2391, 1010, 4689, 1012, 1012, 2800, 2069, 1999, 11829, 2483, 1050, 2307, 2088, 2474, 1041, 28305, 1012, 1012, 1012, 25022, 2638, 2045, 2288, 26297, 28194, 1012, 1012, 1012]\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Check maximum length of a sentence"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "wVT0m8T7evoz"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"max_len = 0\n",
|
|||
|
|
"\n",
|
|||
|
|
"for sentence in dataset['train']:\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": "cmUVPrQYez3J",
|
|||
|
|
"outputId": "ea16509d-6299-4cd8-e028-e32606553a19"
|
|||
|
|
},
|
|||
|
|
"execution_count": 7,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Max sentence length: 238\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Special tokenization"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "2NfXDfYifX5S"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"input_ids = []\n",
|
|||
|
|
"attention_masks = []\n",
|
|||
|
|
"\n",
|
|||
|
|
"for sentence in dataset['train']:\n",
|
|||
|
|
" encoded_dict = tokenizer.encode_plus(\n",
|
|||
|
|
" sentence['sms'],\n",
|
|||
|
|
" add_special_tokens = True,\n",
|
|||
|
|
" max_length = 240,\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",
|
|||
|
|
" attention_masks.append(encoded_dict['attention_mask'])\n",
|
|||
|
|
"\n",
|
|||
|
|
"input_ids = torch.cat(input_ids, dim=0)\n",
|
|||
|
|
"attention_masks = torch.cat(attention_masks, dim=0)\n",
|
|||
|
|
"labels = torch.tensor([sentence['label'] for sentence in dataset['train']])\n",
|
|||
|
|
"\n",
|
|||
|
|
"print('Original: ', dataset['train'][0])\n",
|
|||
|
|
"print('Token IDs:', input_ids[0])"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "4u03dIS1fbKU",
|
|||
|
|
"outputId": "27376a8d-a0ff-4afe-a3fc-01ff3475599e"
|
|||
|
|
},
|
|||
|
|
"execution_count": 8,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Original: {'sms': 'Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\\n', 'label': 0}\n",
|
|||
|
|
"Token IDs: tensor([ 101, 2175, 2127, 18414, 17583, 2391, 1010, 4689, 1012, 1012,\n",
|
|||
|
|
" 2800, 2069, 1999, 11829, 2483, 1050, 2307, 2088, 2474, 1041,\n",
|
|||
|
|
" 28305, 1012, 1012, 1012, 25022, 2638, 2045, 2288, 26297, 28194,\n",
|
|||
|
|
" 1012, 1012, 1012, 102, 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"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Split dataset"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "Z6cC0YjAhmw_"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"dataset = TensorDataset(input_ids, attention_masks, labels)\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": "vH3yXhA0hT3n",
|
|||
|
|
"outputId": "1023a01b-60b0-4de0-ba97-28518b935a21"
|
|||
|
|
},
|
|||
|
|
"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 = 32\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": "830f843f-f1ab-47ee-def7-0dfa3943b264"
|
|||
|
|
},
|
|||
|
|
"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 BERT model"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "o-YrojT-iIfY"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"model = BertForSequenceClassification.from_pretrained(\n",
|
|||
|
|
" \"bert-base-uncased\",\n",
|
|||
|
|
" num_labels = 2,\n",
|
|||
|
|
" output_attentions = False,\n",
|
|||
|
|
" output_hidden_states = False,\n",
|
|||
|
|
")\n",
|
|||
|
|
"\n",
|
|||
|
|
"model.cuda()"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 1000,
|
|||
|
|
"referenced_widgets": [
|
|||
|
|
"371154699498422189229c97ccbfa508",
|
|||
|
|
"51de31720ffc40ba8067dd2c2033851b",
|
|||
|
|
"8c1eaf11c9db4c09aa6936a201615412",
|
|||
|
|
"57ac64a5a8534de889c977d558fb81b8",
|
|||
|
|
"7fb1c882a0524cbd8a71ab42cf54d02a",
|
|||
|
|
"981ae04516214af6995cd9f846f5f45a",
|
|||
|
|
"815a8f36e137412aa54aa012adb7306d",
|
|||
|
|
"83723a5f3104486193880e58b7e9228c",
|
|||
|
|
"9947d9cd0a124c26b132c75e3bcafd2b",
|
|||
|
|
"a323a1744edb4e84b7f2b80530abc097",
|
|||
|
|
"665af58d5b5349f1a85dd4493407020f"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"id": "sIP3VGZmiK9s",
|
|||
|
|
"outputId": "2f4e0a13-f379-4033-cca7-92dfc0155cd5"
|
|||
|
|
},
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"Downloading (…)\"pytorch_model.bin\";: 0%| | 0.00/440M [00:00<?, ?B/s]"
|
|||
|
|
],
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0,
|
|||
|
|
"model_id": "371154699498422189229c97ccbfa508"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stderr",
|
|||
|
|
"text": [
|
|||
|
|
"Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias']\n",
|
|||
|
|
"- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
|
|||
|
|
"- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
|
|||
|
|
"Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
|
|||
|
|
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"BertForSequenceClassification(\n",
|
|||
|
|
" (bert): BertModel(\n",
|
|||
|
|
" (embeddings): BertEmbeddings(\n",
|
|||
|
|
" (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
|
|||
|
|
" (position_embeddings): Embedding(512, 768)\n",
|
|||
|
|
" (token_type_embeddings): Embedding(2, 768)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (encoder): BertEncoder(\n",
|
|||
|
|
" (layer): ModuleList(\n",
|
|||
|
|
" (0): BertLayer(\n",
|
|||
|
|
" (attention): BertAttention(\n",
|
|||
|
|
" (self): BertSelfAttention(\n",
|
|||
|
|
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (output): BertSelfOutput(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (intermediate): BertIntermediate(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
|
|||
|
|
" (intermediate_act_fn): GELUActivation()\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (output): BertOutput(\n",
|
|||
|
|
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (1): BertLayer(\n",
|
|||
|
|
" (attention): BertAttention(\n",
|
|||
|
|
" (self): BertSelfAttention(\n",
|
|||
|
|
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (output): BertSelfOutput(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (intermediate): BertIntermediate(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
|
|||
|
|
" (intermediate_act_fn): GELUActivation()\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (output): BertOutput(\n",
|
|||
|
|
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (2): BertLayer(\n",
|
|||
|
|
" (attention): BertAttention(\n",
|
|||
|
|
" (self): BertSelfAttention(\n",
|
|||
|
|
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (output): BertSelfOutput(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (intermediate): BertIntermediate(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
|
|||
|
|
" (intermediate_act_fn): GELUActivation()\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (output): BertOutput(\n",
|
|||
|
|
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (3): BertLayer(\n",
|
|||
|
|
" (attention): BertAttention(\n",
|
|||
|
|
" (self): BertSelfAttention(\n",
|
|||
|
|
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (output): BertSelfOutput(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (intermediate): BertIntermediate(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
|
|||
|
|
" (intermediate_act_fn): GELUActivation()\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (output): BertOutput(\n",
|
|||
|
|
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (4): BertLayer(\n",
|
|||
|
|
" (attention): BertAttention(\n",
|
|||
|
|
" (self): BertSelfAttention(\n",
|
|||
|
|
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (output): BertSelfOutput(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (intermediate): BertIntermediate(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
|
|||
|
|
" (intermediate_act_fn): GELUActivation()\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (output): BertOutput(\n",
|
|||
|
|
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (5): BertLayer(\n",
|
|||
|
|
" (attention): BertAttention(\n",
|
|||
|
|
" (self): BertSelfAttention(\n",
|
|||
|
|
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (output): BertSelfOutput(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (intermediate): BertIntermediate(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
|
|||
|
|
" (intermediate_act_fn): GELUActivation()\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (output): BertOutput(\n",
|
|||
|
|
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (6): BertLayer(\n",
|
|||
|
|
" (attention): BertAttention(\n",
|
|||
|
|
" (self): BertSelfAttention(\n",
|
|||
|
|
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (output): BertSelfOutput(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (intermediate): BertIntermediate(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
|
|||
|
|
" (intermediate_act_fn): GELUActivation()\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (output): BertOutput(\n",
|
|||
|
|
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (7): BertLayer(\n",
|
|||
|
|
" (attention): BertAttention(\n",
|
|||
|
|
" (self): BertSelfAttention(\n",
|
|||
|
|
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (output): BertSelfOutput(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (intermediate): BertIntermediate(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
|
|||
|
|
" (intermediate_act_fn): GELUActivation()\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (output): BertOutput(\n",
|
|||
|
|
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (8): BertLayer(\n",
|
|||
|
|
" (attention): BertAttention(\n",
|
|||
|
|
" (self): BertSelfAttention(\n",
|
|||
|
|
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (output): BertSelfOutput(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (intermediate): BertIntermediate(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
|
|||
|
|
" (intermediate_act_fn): GELUActivation()\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (output): BertOutput(\n",
|
|||
|
|
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (9): BertLayer(\n",
|
|||
|
|
" (attention): BertAttention(\n",
|
|||
|
|
" (self): BertSelfAttention(\n",
|
|||
|
|
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (output): BertSelfOutput(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (intermediate): BertIntermediate(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
|
|||
|
|
" (intermediate_act_fn): GELUActivation()\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (output): BertOutput(\n",
|
|||
|
|
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (10): BertLayer(\n",
|
|||
|
|
" (attention): BertAttention(\n",
|
|||
|
|
" (self): BertSelfAttention(\n",
|
|||
|
|
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (output): BertSelfOutput(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (intermediate): BertIntermediate(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
|
|||
|
|
" (intermediate_act_fn): GELUActivation()\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (output): BertOutput(\n",
|
|||
|
|
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (11): BertLayer(\n",
|
|||
|
|
" (attention): BertAttention(\n",
|
|||
|
|
" (self): BertSelfAttention(\n",
|
|||
|
|
" (query): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (key): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (value): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (output): BertSelfOutput(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (intermediate): BertIntermediate(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
|
|||
|
|
" (intermediate_act_fn): GELUActivation()\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (output): BertOutput(\n",
|
|||
|
|
" (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
|
|||
|
|
" (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (pooler): BertPooler(\n",
|
|||
|
|
" (dense): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (activation): Tanh()\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
|
|||
|
|
")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 12
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Model architecture"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "l5WEUOO_igvM"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"params = list(model.named_parameters())\n",
|
|||
|
|
"\n",
|
|||
|
|
"print('The BERT model has {:} different named parameters.\\n'.format(len(params)))\n",
|
|||
|
|
"\n",
|
|||
|
|
"print('==== Embedding Layer ====\\n')\n",
|
|||
|
|
"\n",
|
|||
|
|
"for p in params[0:5]:\n",
|
|||
|
|
" print(\"{:<55} {:>12}\".format(p[0], str(tuple(p[1].size()))))\n",
|
|||
|
|
"\n",
|
|||
|
|
"print('\\n==== First Transformer ====\\n')\n",
|
|||
|
|
"\n",
|
|||
|
|
"for p in params[5:21]:\n",
|
|||
|
|
" print(\"{:<55} {:>12}\".format(p[0], str(tuple(p[1].size()))))\n",
|
|||
|
|
"\n",
|
|||
|
|
"print('\\n==== Output Layer ====\\n')\n",
|
|||
|
|
"\n",
|
|||
|
|
"for p in params[-4:]:\n",
|
|||
|
|
" print(\"{:<55} {:>12}\".format(p[0], str(tuple(p[1].size()))))"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "QRAQLbNuigcW",
|
|||
|
|
"outputId": "b01b3fc8-1c72-4529-bd88-cc019c097361"
|
|||
|
|
},
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"The BERT model has 201 different named parameters.\n",
|
|||
|
|
"\n",
|
|||
|
|
"==== Embedding Layer ====\n",
|
|||
|
|
"\n",
|
|||
|
|
"bert.embeddings.word_embeddings.weight (30522, 768)\n",
|
|||
|
|
"bert.embeddings.position_embeddings.weight (512, 768)\n",
|
|||
|
|
"bert.embeddings.token_type_embeddings.weight (2, 768)\n",
|
|||
|
|
"bert.embeddings.LayerNorm.weight (768,)\n",
|
|||
|
|
"bert.embeddings.LayerNorm.bias (768,)\n",
|
|||
|
|
"\n",
|
|||
|
|
"==== First Transformer ====\n",
|
|||
|
|
"\n",
|
|||
|
|
"bert.encoder.layer.0.attention.self.query.weight (768, 768)\n",
|
|||
|
|
"bert.encoder.layer.0.attention.self.query.bias (768,)\n",
|
|||
|
|
"bert.encoder.layer.0.attention.self.key.weight (768, 768)\n",
|
|||
|
|
"bert.encoder.layer.0.attention.self.key.bias (768,)\n",
|
|||
|
|
"bert.encoder.layer.0.attention.self.value.weight (768, 768)\n",
|
|||
|
|
"bert.encoder.layer.0.attention.self.value.bias (768,)\n",
|
|||
|
|
"bert.encoder.layer.0.attention.output.dense.weight (768, 768)\n",
|
|||
|
|
"bert.encoder.layer.0.attention.output.dense.bias (768,)\n",
|
|||
|
|
"bert.encoder.layer.0.attention.output.LayerNorm.weight (768,)\n",
|
|||
|
|
"bert.encoder.layer.0.attention.output.LayerNorm.bias (768,)\n",
|
|||
|
|
"bert.encoder.layer.0.intermediate.dense.weight (3072, 768)\n",
|
|||
|
|
"bert.encoder.layer.0.intermediate.dense.bias (3072,)\n",
|
|||
|
|
"bert.encoder.layer.0.output.dense.weight (768, 3072)\n",
|
|||
|
|
"bert.encoder.layer.0.output.dense.bias (768,)\n",
|
|||
|
|
"bert.encoder.layer.0.output.LayerNorm.weight (768,)\n",
|
|||
|
|
"bert.encoder.layer.0.output.LayerNorm.bias (768,)\n",
|
|||
|
|
"\n",
|
|||
|
|
"==== Output Layer ====\n",
|
|||
|
|
"\n",
|
|||
|
|
"bert.pooler.dense.weight (768, 768)\n",
|
|||
|
|
"bert.pooler.dense.bias (768,)\n",
|
|||
|
|
"classifier.weight (2, 768)\n",
|
|||
|
|
"classifier.bias (2,)\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Init training parameters"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "NZDC4iiQizdX"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"optimizer = torch.optim.AdamW(model.parameters(),\n",
|
|||
|
|
" lr = 2e-5,\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": "_uffUPNEi3S5"
|
|||
|
|
},
|
|||
|
|
"execution_count": 14,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Helper functions"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "bnAwgfZekeYD"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"def flat_accuracy(preds, labels):\n",
|
|||
|
|
" pred_flat = np.argmax(preds, axis=1).flatten()\n",
|
|||
|
|
" labels_flat = labels.flatten()\n",
|
|||
|
|
" return np.sum(pred_flat == labels_flat) / len(labels_flat)\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",
|
|||
|
|
" \n",
|
|||
|
|
" return str(datetime.timedelta(seconds=elapsed_rounded))"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "Z3XSZuFmkgVr"
|
|||
|
|
},
|
|||
|
|
"execution_count": 15,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Training"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "L-ZeLPfbkqy9"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"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",
|
|||
|
|
"\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",
|
|||
|
|
" 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",
|
|||
|
|
"\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": "QZ9H2EJNksT_",
|
|||
|
|
"outputId": "7c5d39fb-13d3-48c0-8d04-1dba8740bfcd"
|
|||
|
|
},
|
|||
|
|
"execution_count": 16,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"\n",
|
|||
|
|
"======== Epoch 1 / 4 ========\n",
|
|||
|
|
"Training...\n",
|
|||
|
|
" Batch 40 of 129. Elapsed: 0:00:49.\n",
|
|||
|
|
" Batch 80 of 129. Elapsed: 0:01:34.\n",
|
|||
|
|
" Batch 120 of 129. Elapsed: 0:02:19.\n",
|
|||
|
|
"\n",
|
|||
|
|
" Average training loss: 0.11\n",
|
|||
|
|
" Training epcoh took: 0:02:29\n",
|
|||
|
|
"\n",
|
|||
|
|
"Running Validation...\n",
|
|||
|
|
" Accuracy: 0.99\n",
|
|||
|
|
" Validation Loss: 0.07\n",
|
|||
|
|
" Validation took: 0:00:06\n",
|
|||
|
|
"\n",
|
|||
|
|
"======== Epoch 2 / 4 ========\n",
|
|||
|
|
"Training...\n",
|
|||
|
|
" Batch 40 of 129. Elapsed: 0:00:46.\n",
|
|||
|
|
" Batch 80 of 129. Elapsed: 0:01:30.\n",
|
|||
|
|
" Batch 120 of 129. Elapsed: 0:02:15.\n",
|
|||
|
|
"\n",
|
|||
|
|
" Average training loss: 0.02\n",
|
|||
|
|
" Training epcoh took: 0:02:25\n",
|
|||
|
|
"\n",
|
|||
|
|
"Running Validation...\n",
|
|||
|
|
" Accuracy: 0.99\n",
|
|||
|
|
" Validation Loss: 0.08\n",
|
|||
|
|
" Validation took: 0:00:06\n",
|
|||
|
|
"\n",
|
|||
|
|
"======== Epoch 3 / 4 ========\n",
|
|||
|
|
"Training...\n",
|
|||
|
|
" Batch 40 of 129. Elapsed: 0:00:45.\n",
|
|||
|
|
" Batch 80 of 129. Elapsed: 0:01:30.\n",
|
|||
|
|
" Batch 120 of 129. Elapsed: 0:02:15.\n",
|
|||
|
|
"\n",
|
|||
|
|
" Average training loss: 0.00\n",
|
|||
|
|
" Training epcoh took: 0:02:25\n",
|
|||
|
|
"\n",
|
|||
|
|
"Running Validation...\n",
|
|||
|
|
" Accuracy: 0.98\n",
|
|||
|
|
" Validation Loss: 0.10\n",
|
|||
|
|
" Validation took: 0:00:06\n",
|
|||
|
|
"\n",
|
|||
|
|
"======== Epoch 4 / 4 ========\n",
|
|||
|
|
"Training...\n",
|
|||
|
|
" Batch 40 of 129. Elapsed: 0:00:45.\n",
|
|||
|
|
" Batch 80 of 129. Elapsed: 0:01:30.\n",
|
|||
|
|
" Batch 120 of 129. Elapsed: 0:02:15.\n",
|
|||
|
|
"\n",
|
|||
|
|
" Average training loss: 0.00\n",
|
|||
|
|
" Training epcoh took: 0:02:25\n",
|
|||
|
|
"\n",
|
|||
|
|
"Running Validation...\n",
|
|||
|
|
" Accuracy: 0.99\n",
|
|||
|
|
" Validation Loss: 0.09\n",
|
|||
|
|
" Validation took: 0:00:06\n",
|
|||
|
|
"\n",
|
|||
|
|
"Training complete!\n",
|
|||
|
|
"Total training took 0:10:06 (h:mm:ss)\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Train summary"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "eZ1fmJMjrRgc"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"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": "w4ov2mClrLGW",
|
|||
|
|
"outputId": "ad5057e3-f0e5-44c0-8c5a-bf4d69c600ab"
|
|||
|
|
},
|
|||
|
|
"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 1.07e-01 0.07 0.99 0:02:29 0:00:06\n",
|
|||
|
|
"2 1.89e-02 0.08 0.99 0:02:25 0:00:06\n",
|
|||
|
|
"3 4.73e-03 0.10 0.98 0:02:25 0:00:06\n",
|
|||
|
|
"4 1.93e-03 0.09 0.99 0:02:25 0:00:06"
|
|||
|
|
],
|
|||
|
|
"text/html": [
|
|||
|
|
"\n",
|
|||
|
|
" <div id=\"df-ee4b2d83-3992-4ef7-9181-ffeb684a31a1\">\n",
|
|||
|
|
" <div class=\"colab-df-container\">\n",
|
|||
|
|
" <div>\n",
|
|||
|
|
"<style scoped>\n",
|
|||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|||
|
|
" vertical-align: middle;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe tbody tr th {\n",
|
|||
|
|
" vertical-align: top;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .dataframe thead th {\n",
|
|||
|
|
" text-align: right;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"</style>\n",
|
|||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|||
|
|
" <thead>\n",
|
|||
|
|
" <tr style=\"text-align: right;\">\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th>Training Loss</th>\n",
|
|||
|
|
" <th>Valid. Loss</th>\n",
|
|||
|
|
" <th>Valid. Accur.</th>\n",
|
|||
|
|
" <th>Training Time</th>\n",
|
|||
|
|
" <th>Validation Time</th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>epoch</th>\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" <th></th>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </thead>\n",
|
|||
|
|
" <tbody>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>1</th>\n",
|
|||
|
|
" <td>1.07e-01</td>\n",
|
|||
|
|
" <td>0.07</td>\n",
|
|||
|
|
" <td>0.99</td>\n",
|
|||
|
|
" <td>0:02:29</td>\n",
|
|||
|
|
" <td>0:00:06</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>1.89e-02</td>\n",
|
|||
|
|
" <td>0.08</td>\n",
|
|||
|
|
" <td>0.99</td>\n",
|
|||
|
|
" <td>0:02:25</td>\n",
|
|||
|
|
" <td>0:00:06</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>4.73e-03</td>\n",
|
|||
|
|
" <td>0.10</td>\n",
|
|||
|
|
" <td>0.98</td>\n",
|
|||
|
|
" <td>0:02:25</td>\n",
|
|||
|
|
" <td>0:00:06</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>1.93e-03</td>\n",
|
|||
|
|
" <td>0.09</td>\n",
|
|||
|
|
" <td>0.99</td>\n",
|
|||
|
|
" <td>0:02:25</td>\n",
|
|||
|
|
" <td>0:00:06</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>\n",
|
|||
|
|
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ee4b2d83-3992-4ef7-9181-ffeb684a31a1')\"\n",
|
|||
|
|
" title=\"Convert this dataframe to an interactive table.\"\n",
|
|||
|
|
" style=\"display:none;\">\n",
|
|||
|
|
" \n",
|
|||
|
|
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
|
|||
|
|
" width=\"24px\">\n",
|
|||
|
|
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
|
|||
|
|
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
|
|||
|
|
" </svg>\n",
|
|||
|
|
" </button>\n",
|
|||
|
|
" \n",
|
|||
|
|
" <style>\n",
|
|||
|
|
" .colab-df-container {\n",
|
|||
|
|
" display:flex;\n",
|
|||
|
|
" flex-wrap:wrap;\n",
|
|||
|
|
" gap: 12px;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .colab-df-convert {\n",
|
|||
|
|
" background-color: #E8F0FE;\n",
|
|||
|
|
" border: none;\n",
|
|||
|
|
" border-radius: 50%;\n",
|
|||
|
|
" cursor: pointer;\n",
|
|||
|
|
" display: none;\n",
|
|||
|
|
" fill: #1967D2;\n",
|
|||
|
|
" height: 32px;\n",
|
|||
|
|
" padding: 0 0 0 0;\n",
|
|||
|
|
" width: 32px;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" .colab-df-convert:hover {\n",
|
|||
|
|
" background-color: #E2EBFA;\n",
|
|||
|
|
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
|||
|
|
" fill: #174EA6;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" [theme=dark] .colab-df-convert {\n",
|
|||
|
|
" background-color: #3B4455;\n",
|
|||
|
|
" fill: #D2E3FC;\n",
|
|||
|
|
" }\n",
|
|||
|
|
"\n",
|
|||
|
|
" [theme=dark] .colab-df-convert:hover {\n",
|
|||
|
|
" background-color: #434B5C;\n",
|
|||
|
|
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
|||
|
|
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
|||
|
|
" fill: #FFFFFF;\n",
|
|||
|
|
" }\n",
|
|||
|
|
" </style>\n",
|
|||
|
|
"\n",
|
|||
|
|
" <script>\n",
|
|||
|
|
" const buttonEl =\n",
|
|||
|
|
" document.querySelector('#df-ee4b2d83-3992-4ef7-9181-ffeb684a31a1 button.colab-df-convert');\n",
|
|||
|
|
" buttonEl.style.display =\n",
|
|||
|
|
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
|||
|
|
"\n",
|
|||
|
|
" async function convertToInteractive(key) {\n",
|
|||
|
|
" const element = document.querySelector('#df-ee4b2d83-3992-4ef7-9181-ffeb684a31a1');\n",
|
|||
|
|
" const dataTable =\n",
|
|||
|
|
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
|
|||
|
|
" [key], {});\n",
|
|||
|
|
" if (!dataTable) return;\n",
|
|||
|
|
"\n",
|
|||
|
|
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
|
|||
|
|
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
|||
|
|
" + ' to learn more about interactive tables.';\n",
|
|||
|
|
" element.innerHTML = '';\n",
|
|||
|
|
" dataTable['output_type'] = 'display_data';\n",
|
|||
|
|
" await google.colab.output.renderOutput(dataTable, element);\n",
|
|||
|
|
" const docLink = document.createElement('div');\n",
|
|||
|
|
" docLink.innerHTML = docLinkHtml;\n",
|
|||
|
|
" element.appendChild(docLink);\n",
|
|||
|
|
" }\n",
|
|||
|
|
" </script>\n",
|
|||
|
|
" </div>\n",
|
|||
|
|
" </div>\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": "4Jg3MOeZrTf9",
|
|||
|
|
"outputId": "1782176d-be6e-43c8-8903-d1e698d3d700"
|
|||
|
|
},
|
|||
|
|
"execution_count": 18,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 864x432 with 1 Axes>"
|
|||
|
|
],
|
|||
|
|
"image/png": "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
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Create test loader"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "kn87qhtR9MP4"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"prediction_dataloader = DataLoader(\n",
|
|||
|
|
" test_dataset,\n",
|
|||
|
|
" sampler = SequentialSampler(test_dataset),\n",
|
|||
|
|
" batch_size = batch_size\n",
|
|||
|
|
" )"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "ENqiBZio9a7a"
|
|||
|
|
},
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Evaluate on test dataset"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "dAF9Iqol-kAP"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"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": "XXCWIk8c9Oun",
|
|||
|
|
"outputId": "ad95fd9e-f132-4cef-d848-57d126687d8b"
|
|||
|
|
},
|
|||
|
|
"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": "TVIFBFmFEwNv",
|
|||
|
|
"outputId": "8d0a7751-cb1b-40ed-a705-40944ec75309"
|
|||
|
|
},
|
|||
|
|
"execution_count": 21,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Correct predictions: 994, incorrect results: 6, accuracy: 0.994\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# MCC Score"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "6gTgKchs_OXY"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"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": "hOelDEdn_QDE",
|
|||
|
|
"outputId": "022d5656-09cb-4279-878e-13611ceb68fc"
|
|||
|
|
},
|
|||
|
|
"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": "cF34A88N_UgQ",
|
|||
|
|
"outputId": "439ba21c-94a3-4a7b-bbc8-c2b828dbc6ba"
|
|||
|
|
},
|
|||
|
|
"execution_count": 23,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 864x432 with 1 Axes>"
|
|||
|
|
],
|
|||
|
|
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAuUAAAGaCAYAAACopj13AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzde3yMZ/7/8ffkLAkSaaKKoIg4xbGUUqVo1JlQiji0qNJt9asbtr92d7vdUlR10RZtldAqkkixDqXdHpyLChWKOoRsmSIhiUgk8/vDV76NScYkmXGTvJ6Ph8fKdd/X5/5MJmbfuXvNNSaLxWIRAAAAAMO4GN0AAAAAUNYRygEAAACDEcoBAAAAgxHKAQAAAIMRygEAAACDEcoBAAAAgxHKAQC4SwwbNkydOnUyug0ABnAzugEAKKmdO3cqMjJSkjRkyBC9/vrrVudcuHBBHTp0UHZ2tlq1aqXo6Gircw4cOKBly5Zp9+7dMpvNcnFxUbVq1dSmTRsNGjRItWvXznf+1atX9cUXX2jTpk06duyY0tPTVbFiRTVs2FDdunVTr1695OZm+2X2ypUrio6O1saNG3X27Fnl5OTI399foaGh6tixowYMGFCC7wxu1alTJ509ezbva5PJpICAANWqVUuDBw9W9+7di1178+bNSkxM1AsvvOCIVgGUMYRyAKWGp6en1q5dq8mTJ8vDwyPfsfj4eFkslkJD8ty5czV37lz5+/urR48eqlOnjnJzc3Xs2DGtX79ey5Yt065du+Tr6ytJOnXqlMaMGaOTJ0+qbdu2GjNmjPz9/XXhwgVt375dU6ZM0bFjx/TnP/+50H7T0tIUERGhpKQkPfHEE+rfv7/c3d2VlJSkvXv3asmSJYRyJ7j//vv18ssvS5Jyc3N17tw5xcXF6eWXX5bZbNaIESOKVXfz5s2Ki4sjlAMoFkI5gFKjS5cuWrt2rTZv3qwnn3wy37HY2Fg9+uij2rFjh9W8VatWac6cOWrdurXmzZun8uXL5zv+yiuvaO7cuXlfZ2ZmauzYsTpz5ozmzJmjrl275jt/zJgxSkhI0IEDB2z2u2LFCp08eVJ/+ctfNHz4cKvjZrP5to/ZGdLS0vJ++biXWCwWZWRkyMfHx+Z55cuXV+/evfONPfXUU2rfvr1iY2OLHcoBoCRYUw6g1GjQoIHq1aun2NjYfOMJCQk6evSo+vfvbzUnKytLs2fPlre3t2bPnm0VyCXJy8tLkyZNyguqK1eu1IkTJzRy5EirQH5TWFiYhgwZYrPfkydPSpLatGlT4PHAwECrsVOnTmnKlCl69NFH1ahRI7Vr107jxo3TwYMH8523efNmDRo0SE2bNlWzZs00aNAgbd682apep06dNGzYMB06dEjPPPOMWrRooV69euXr8ZVXXlG7du3UqFEjderUSW+//bYyMjJsPrZb6//888+KjIxUs2bN1KpVK0VFRenChQtW52dlZenDDz9U9+7d1bhxY7Vs2VLPPfecDh06lO+8nTt35j3Xy5Yt05NPPqnGjRvrk08+sauvW1WsWFEeHh5yd3fPN56QkKDJkyfriSeeUJMmTfK+l1999VW+84YNG6a4uDhJUr169fL+/PFn0Ww2680339Tjjz+uRo0aqU2bNho5cqS2bt1q1c+5c+f08ssv66GHHlKTJk30zDPP6MSJE8V6bADuDdwpB1Cq9O/fX9OmTdO5c+dUuXJlSTfuhAcEBOixxx6zOn/v3r0ym83q3bu3KlWqZNc1Nm7cKOnG3dWSCA4OlnTjLv6kSZNuu/78wIEDGjFihK5fv66IiAjVrVtXqamp2rVrl/bt26dGjRpJkpYtW6Y33nhDDz74oJ5//nlJUlxcnMaPH6833njDqu/k5GQNHz5c4eHh6tq1a17gPnjwoIYPH64KFSroqaeeUuXKlXX48GFFR0dr3759io6OtgqxBfntt980YsQIde3aVU888YQOHTqkmJgYHTx4UKtWrVK5cuUkSdnZ2XrmmWe0b98+9e7dW0OGDFFaWppWrFihwYMHa+nSpWrcuHG+2osXL1ZKSooGDBigwMBA3X///bftJycnRxcvXpR0Y/mK2WzWkiVLlJ6erkGDBuU796uvvtKvv/6q8PBwVa1aVSkpKYqLi9OECRM0c+ZM9ezZU5L03HPPKTc3Vz/++KOmT5+eN7958+aSpDNnzmjw4MG6cOGCevfurUaNGunq1avav3+/tm3bpkceeSRvTkZGhoYOHaomTZpo4sSJOnPmjJYsWaLnn39ea9eulaur620fI4B7kAUA7nE7duywhISEWD766CPLxYsXLQ0bNrR88MEHFovFYrl69aqlRYsWlmnTplksFouladOmlqFDh+bNXbJkiSUkJMTyySef2H29Vq1aWZo3b17ivlNSUiwdOnSwhISEWNq0aWN54YUXLPPnz7fs3r3bkpOTk+/c3NxcS/fu3S2NGjWyJCYmWtW6eX5KSoqladOmls6dO1uuXLmSd/zKlSuWxx9/3NK0aVNLampq3njHjh0tISEhlhUrVljV7Nmzp+WJJ57IV8disVg2bdpkCQkJscTExNz2Md6sv2jRonzjixYtsoSEhFjmz59vNfbdd9/lO/fKlSuWDh065Hvebj7nDz30kOX333+/bR+39nPrn8aNG1uWL19udX56errVWEZGhqVr166Wbt265RuPioqyhISEFHjdZ599tsDHZrFY8j3XQ4cOtYSEhFgWLFiQ75yFCxcWOh9A6cDyFQClir+/vzp16pS3lGDTpk26cuVKgUtXpBvrpyUVaQ11Wlrabdct26NixYqKjY3V6NGjVb58eW3cuFHvvPOOhgwZos6dO+uHH37IOzcxMVFHjx5Vv379FBoaalXLxeXGy/nWrVuVkZGhYcOG5XtMvr6+GjZsmDIyMrRt27Z8c/38/NSvX798Y0eOHNGRI0fUo0cPZWVl6eLFi3l/WrRoIW9v7wKXXRTE19dXTz/9dL6xp59+Wr6+vvmWgXz55Zd68MEH1bBhw3zXy8rKUtu2bbVnzx5lZmbmq9O7d28FBATY1cdNVatW1aJFi7Ro0SJ98sknmjZtmpo0aaK//e1viomJyXeut7d33t+vXr2qS5cu6erVq3r44Yd1/PjxvJ8fW1JSUvT999+rffv2at++vdXxm8/dH7++uZvQTQ8//LCkG8uXAJROLF8BUOr0799fY8aM0Y8//qiYmBiFhYWpTp06BZ57M7imp6fbXd/X17dI59tSqVIlTZo0SZMmTdKlS5f0008/af369fryyy81YcIExcfHq0aNGnnrzxs0aGCz3pkzZyRJdevWtTp2cywpKSnfePXq1a2WRBw/flySNGfOHM2ZM6fAa/3++++3f4D/W//W3XA8PDxUvXr1fL0cP35cmZmZha6xl6RLly6pSpUqeV/XrFnTrh7+yNvbW23bts031rNnT/Xt21dvvvmmOnXqJH9/f0k3ttKcPXu2tmzZUuAa+MuXL9/2F7rTp0/LYrHc9rm7KSgoSJ6envnG/Pz8JN0I+ABKJ0I5gFKnXbt2qly5subNm6edO3fqb3/7W6Hn3gyqt76R0Ja6detq9+7dSkpKUvXq1Uvabh5/f3917NhRHTt2VJUqVfThhx9q3bp1eevCneXmmu6CjBo1qsC7u5JUoUIFh/ZhsVgUEhKiKVOmFHrOrev+bfVeFG5ubnr44Ye1ZMkSJSQkqEOHDrJYLBo1apSOHz+uyMhINWrUSOXLl5erq6tiYmK0du1a5ebmOuT6f2RrzbjFYnH49QDcHQjlAEodV1dX9enTR/Pnz5eXl5d69OhR6LnNmzdXYGCgNm/erEuXLuXdIbWla9eu2r17t1auXJm337WjNWnSRNKNXTgkqVatWpJuLGOx5eYvCUePHrW643zs2LF859hSo0YNSTeWUtx6V7mokpKSlJWVle9ueVZWlpKSkvTggw/mu+alS5f08MMPWy3puBOuX78u6f/+q8mRI0d0+PBhjR8/Xn/605/ynbty5Uqr+SaTqcC6wcHBMplMt33uAJRtrCkHUCoNGjRIEyZM0
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"flat_predictions = np.concatenate(predictions, axis=0)\n",
|
|||
|
|
"\n",
|
|||
|
|
"flat_predictions = np.argmax(flat_predictions, axis=1).flatten()\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": "vx_SM19o_XqD",
|
|||
|
|
"outputId": "a37b67e7-4b48-4c69-cef1-21dbb0513829"
|
|||
|
|
},
|
|||
|
|
"execution_count": 24,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Total MCC: 0.973\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Save model"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "ZE7xxmFJ-oBM"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"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/BERT_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)\n"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "fJAqcU8F-p2Z",
|
|||
|
|
"outputId": "329483d2-97dd-4f1e-ee10-b94869c8e5ac"
|
|||
|
|
},
|
|||
|
|
"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/BERT_Model\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"('/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/BERT_Model/tokenizer_config.json',\n",
|
|||
|
|
" '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/BERT_Model/special_tokens_map.json',\n",
|
|||
|
|
" '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/BERT_Model/vocab.txt',\n",
|
|||
|
|
" '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/BERT_Model/added_tokens.json')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 25
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Bibliografia\n",
|
|||
|
|
"https://mccormickml.com/2019/07/22/BERT-fine-tuning/#a1-saving--loading-fine-tuned-model"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "6pFz8n_aHca9"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
}
|