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5277 lines
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{
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"# Instalacja pakietów"
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]
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],
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"source": [
|
|||
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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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|||
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|
|||
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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",
|
|||
|
|
"from transformers import GPT2Tokenizer\n",
|
|||
|
|
"import torch\n",
|
|||
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|
"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 GPT2ForSequenceClassification, GPT2Config\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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"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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"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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"422332e84b4e4ce88eb8eae5a927f855",
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"output_type": "stream",
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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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"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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"cell_type": "code",
|
|||
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|
"source": [
|
|||
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|
"dataset['train'][0]"
|
|||
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],
|
|||
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|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
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},
|
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|
"id": "Mf1QIM_dlp2x",
|
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"outputId": "352d6bf6-c5fb-4a20-a653-18fdc5a8610f"
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},
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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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|||
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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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"cell_type": "markdown",
|
|||
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"source": [
|
|||
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"# Tokenizer GPT2"
|
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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 = GPT2Tokenizer.from_pretrained('gpt2')\n",
|
|||
|
|
"tokenizer.pad_token = tokenizer.eos_token\n"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "hmnlC_hubLmP",
|
|||
|
|
"colab": {
|
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|
|
"base_uri": "https://localhost:8080/",
|
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"height": 113,
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"referenced_widgets": [
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"c66f887ac2b347f790613aad99628ddd",
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"48d2febefb344765ba898f66631a20b5",
|
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"ddff15d28609430e9901c59405a081f8",
|
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|
"f3419ef8f62d4229a001e085c02af151",
|
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"d08b4d68348c464cbc9b14d747786fde",
|
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"ea925b20bb6442bcb126a4c152c46c44",
|
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"b2df53a7ccb845d6bb9319daecc1a475",
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"ee7bf6b4fbdd44ec99028b45184b9c31",
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"ac3375bb5681453d8d5d711fbefd4455",
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"39e1df93a3e04d949432e0f12e67bb53",
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"0257dac8f585464daf84fc253f00a156",
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"e13ede98e6d84b3db1ffed7075db1833",
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"99c7e79fb47b46bd94532cd4fe31a154",
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"344ea5fcf09c4c6992e7fd875a24d468",
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"2250f533e2914c52827ba6eca0920c97",
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"94e6951304cc4f74bdfb53b77ba02308",
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"a3329ed7cefd4376956e44c7e04de4d5",
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"a55ce911dc3b43c1bfc830496f1cb6d4",
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"ae46982104714a43ba1bd2c5c6a76ff8",
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"ec15d18ce87a4a7682772debeb9a37ae",
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"b6a13356c2fc4ba09e599d6038625f80",
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"2b24cf0161df478d91710a61af75aab9",
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"1c8e2e670fe94e11905fdb82d5c28aef",
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"88d4f4991f5846639b9216163cf928d7",
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"5d13be7e7854411b84110c6c6aaf6aa7",
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"f1adbed46a7d458ba893ca4e3620b275",
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"72882edb5601464f8c3b6a76fd1c10d7",
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"f27b9257a51f44a1aa816f88e407323b",
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"124f164056d542f2820c1ebb99faeb18",
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"ed544ca048f34d01bffd01109a2dd3e5",
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"904ac8e80dae40f1a4f344a3c32f98cb",
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"e8fbe6c23d84418eb265ebd38dd43007",
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"3e98702f338c4c7ea96ce4fc466e6c52"
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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": {
|
|||
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|
"base_uri": "https://localhost:8080/"
|
|||
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},
|
|||
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|
"id": "ZxigrpcQdWCF",
|
|||
|
|
"outputId": "cf3cd54a-24a7-45db-c1b4-dd772fb10bbf"
|
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},
|
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|
"execution_count": 6,
|
|||
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"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', 'Ġuntil', 'Ġjur', 'ong', 'Ġpoint', ',', 'Ġcrazy', '..', 'ĠAvailable', 'Ġonly', 'Ġin', 'Ġbug', 'is', 'Ġn', 'Ġgreat', 'Ġworld', 'Ġla', 'Ġe', 'Ġbuffet', '...', 'ĠC', 'ine', 'Ġthere', 'Ġgot', 'Ġam', 'ore', 'Ġwat', '...', 'Ċ']\n",
|
|||
|
|
"Token IDs: [5247, 1566, 8174, 506, 966, 11, 7165, 492, 14898, 691, 287, 5434, 271, 299, 1049, 995, 8591, 304, 44703, 986, 327, 500, 612, 1392, 716, 382, 4383, 986, 198]\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/",
|
|||
|
|
"height": 34
|
|||
|
|
},
|
|||
|
|
"id": "cmUVPrQYez3J",
|
|||
|
|
"outputId": "bdea41f3-e599-4387-ef95-2fb5b9a3737d"
|
|||
|
|
},
|
|||
|
|
"execution_count": 7,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Max sentence length: 258\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 = 260,\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": "3940e7ff-aed0-4689-93ff-4298ebfc4db4"
|
|||
|
|
},
|
|||
|
|
"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([ 5247, 1566, 8174, 506, 966, 11, 7165, 492, 14898, 691,\n",
|
|||
|
|
" 287, 5434, 271, 299, 1049, 995, 8591, 304, 44703, 986,\n",
|
|||
|
|
" 327, 500, 612, 1392, 716, 382, 4383, 986, 198, 50256,\n",
|
|||
|
|
" 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n",
|
|||
|
|
" 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n",
|
|||
|
|
" 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n",
|
|||
|
|
" 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n",
|
|||
|
|
" 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n",
|
|||
|
|
" 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n",
|
|||
|
|
" 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n",
|
|||
|
|
" 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n",
|
|||
|
|
" 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n",
|
|||
|
|
" 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n",
|
|||
|
|
" 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n",
|
|||
|
|
" 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n",
|
|||
|
|
" 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n",
|
|||
|
|
" 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n",
|
|||
|
|
" 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n",
|
|||
|
|
" 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n",
|
|||
|
|
" 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n",
|
|||
|
|
" 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n",
|
|||
|
|
" 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n",
|
|||
|
|
" 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n",
|
|||
|
|
" 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n",
|
|||
|
|
" 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256,\n",
|
|||
|
|
" 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256])\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": "fd6c0545-d91a-4920-f961-d758cde83911"
|
|||
|
|
},
|
|||
|
|
"execution_count": 9,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"1,000 test samples\n",
|
|||
|
|
"4,116 training samples\n",
|
|||
|
|
" 458 validation samples\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Create train and validation loaders"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "z1hVsejihpO2"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"batch_size = 8\n",
|
|||
|
|
"\n",
|
|||
|
|
"train_dataloader = DataLoader(\n",
|
|||
|
|
" train_dataset,\n",
|
|||
|
|
" sampler = RandomSampler(train_dataset),\n",
|
|||
|
|
" batch_size = batch_size\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
"validation_dataloader = DataLoader(\n",
|
|||
|
|
" val_dataset,\n",
|
|||
|
|
" sampler = SequentialSampler(val_dataset),\n",
|
|||
|
|
" batch_size = batch_size\n",
|
|||
|
|
" )"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "k4pXght6hre3"
|
|||
|
|
},
|
|||
|
|
"execution_count": 10,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Device check"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "MnErwHAbl_rF"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"if torch.cuda.is_available(): \n",
|
|||
|
|
" device = torch.device(\"cuda\")\n",
|
|||
|
|
"\n",
|
|||
|
|
" print('There are %d GPU(s) available.' % torch.cuda.device_count())\n",
|
|||
|
|
" print('We will use the GPU:', torch.cuda.get_device_name(0))\n",
|
|||
|
|
"\n",
|
|||
|
|
"else:\n",
|
|||
|
|
" print('No GPU available, using the CPU instead.')\n",
|
|||
|
|
" device = torch.device(\"cpu\")"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "aUvyBFxzmBUy",
|
|||
|
|
"outputId": "04bc746e-0d7a-443f-dfd2-df757b49cc04"
|
|||
|
|
},
|
|||
|
|
"execution_count": 11,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"There are 1 GPU(s) available.\n",
|
|||
|
|
"We will use the GPU: Tesla T4\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Load GPT2 model"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "o-YrojT-iIfY"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"model = GPT2ForSequenceClassification.from_pretrained(\n",
|
|||
|
|
" 'gpt2',\n",
|
|||
|
|
" num_labels = 2,\n",
|
|||
|
|
")"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 84,
|
|||
|
|
"referenced_widgets": [
|
|||
|
|
"54a468d755ee4d398517315461829a70",
|
|||
|
|
"7d9cfb12850a48c1a9cfde1118adc2ea",
|
|||
|
|
"2534269fcfad4bc78214a4a606842072",
|
|||
|
|
"b1e3f817928c42e9b69db75289a9d30a",
|
|||
|
|
"91fd0a3f735a4d35a040f7bba80d3e24",
|
|||
|
|
"0ea13a9834954df78af18a82ac52593a",
|
|||
|
|
"65da66d6460c4973a5cf76d626105013",
|
|||
|
|
"1fc49833bbb74b358896cddf45f76efe",
|
|||
|
|
"2b5f3b65686a4794a54a96f48c03902c",
|
|||
|
|
"f009b8b012b24afab7b485ebb31129a5",
|
|||
|
|
"2efae8e22a294bc5a29abedeb136f909"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"id": "sIP3VGZmiK9s",
|
|||
|
|
"outputId": "ebac6f6b-b0c4-49a3-8da7-2c98fe1bbbf1"
|
|||
|
|
},
|
|||
|
|
"execution_count": 12,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"Downloading (…)\"pytorch_model.bin\";: 0%| | 0.00/548M [00:00<?, ?B/s]"
|
|||
|
|
],
|
|||
|
|
"application/vnd.jupyter.widget-view+json": {
|
|||
|
|
"version_major": 2,
|
|||
|
|
"version_minor": 0,
|
|||
|
|
"model_id": "54a468d755ee4d398517315461829a70"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stderr",
|
|||
|
|
"text": [
|
|||
|
|
"Some weights of GPT2ForSequenceClassification were not initialized from the model checkpoint at gpt2 and are newly initialized: ['score.weight']\n",
|
|||
|
|
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"model.resize_token_embeddings(len(tokenizer))\n",
|
|||
|
|
"model.config.pad_token_id = model.config.eos_token_id\n",
|
|||
|
|
"\n",
|
|||
|
|
"model.cuda()"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "LhpRR5YEeU1S",
|
|||
|
|
"outputId": "c1e1d515-12fe-41bf-94b5-c61ad83afbc2"
|
|||
|
|
},
|
|||
|
|
"execution_count": 13,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"GPT2ForSequenceClassification(\n",
|
|||
|
|
" (transformer): GPT2Model(\n",
|
|||
|
|
" (wte): Embedding(50257, 768)\n",
|
|||
|
|
" (wpe): Embedding(1024, 768)\n",
|
|||
|
|
" (drop): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (h): ModuleList(\n",
|
|||
|
|
" (0): GPT2Block(\n",
|
|||
|
|
" (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" (attn): GPT2Attention(\n",
|
|||
|
|
" (c_attn): Conv1D()\n",
|
|||
|
|
" (c_proj): Conv1D()\n",
|
|||
|
|
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" (mlp): GPT2MLP(\n",
|
|||
|
|
" (c_fc): Conv1D()\n",
|
|||
|
|
" (c_proj): Conv1D()\n",
|
|||
|
|
" (act): NewGELUActivation()\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (1): GPT2Block(\n",
|
|||
|
|
" (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" (attn): GPT2Attention(\n",
|
|||
|
|
" (c_attn): Conv1D()\n",
|
|||
|
|
" (c_proj): Conv1D()\n",
|
|||
|
|
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" (mlp): GPT2MLP(\n",
|
|||
|
|
" (c_fc): Conv1D()\n",
|
|||
|
|
" (c_proj): Conv1D()\n",
|
|||
|
|
" (act): NewGELUActivation()\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (2): GPT2Block(\n",
|
|||
|
|
" (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" (attn): GPT2Attention(\n",
|
|||
|
|
" (c_attn): Conv1D()\n",
|
|||
|
|
" (c_proj): Conv1D()\n",
|
|||
|
|
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" (mlp): GPT2MLP(\n",
|
|||
|
|
" (c_fc): Conv1D()\n",
|
|||
|
|
" (c_proj): Conv1D()\n",
|
|||
|
|
" (act): NewGELUActivation()\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (3): GPT2Block(\n",
|
|||
|
|
" (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" (attn): GPT2Attention(\n",
|
|||
|
|
" (c_attn): Conv1D()\n",
|
|||
|
|
" (c_proj): Conv1D()\n",
|
|||
|
|
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" (mlp): GPT2MLP(\n",
|
|||
|
|
" (c_fc): Conv1D()\n",
|
|||
|
|
" (c_proj): Conv1D()\n",
|
|||
|
|
" (act): NewGELUActivation()\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (4): GPT2Block(\n",
|
|||
|
|
" (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" (attn): GPT2Attention(\n",
|
|||
|
|
" (c_attn): Conv1D()\n",
|
|||
|
|
" (c_proj): Conv1D()\n",
|
|||
|
|
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" (mlp): GPT2MLP(\n",
|
|||
|
|
" (c_fc): Conv1D()\n",
|
|||
|
|
" (c_proj): Conv1D()\n",
|
|||
|
|
" (act): NewGELUActivation()\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (5): GPT2Block(\n",
|
|||
|
|
" (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" (attn): GPT2Attention(\n",
|
|||
|
|
" (c_attn): Conv1D()\n",
|
|||
|
|
" (c_proj): Conv1D()\n",
|
|||
|
|
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" (mlp): GPT2MLP(\n",
|
|||
|
|
" (c_fc): Conv1D()\n",
|
|||
|
|
" (c_proj): Conv1D()\n",
|
|||
|
|
" (act): NewGELUActivation()\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (6): GPT2Block(\n",
|
|||
|
|
" (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" (attn): GPT2Attention(\n",
|
|||
|
|
" (c_attn): Conv1D()\n",
|
|||
|
|
" (c_proj): Conv1D()\n",
|
|||
|
|
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" (mlp): GPT2MLP(\n",
|
|||
|
|
" (c_fc): Conv1D()\n",
|
|||
|
|
" (c_proj): Conv1D()\n",
|
|||
|
|
" (act): NewGELUActivation()\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (7): GPT2Block(\n",
|
|||
|
|
" (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" (attn): GPT2Attention(\n",
|
|||
|
|
" (c_attn): Conv1D()\n",
|
|||
|
|
" (c_proj): Conv1D()\n",
|
|||
|
|
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" (mlp): GPT2MLP(\n",
|
|||
|
|
" (c_fc): Conv1D()\n",
|
|||
|
|
" (c_proj): Conv1D()\n",
|
|||
|
|
" (act): NewGELUActivation()\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (8): GPT2Block(\n",
|
|||
|
|
" (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" (attn): GPT2Attention(\n",
|
|||
|
|
" (c_attn): Conv1D()\n",
|
|||
|
|
" (c_proj): Conv1D()\n",
|
|||
|
|
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" (mlp): GPT2MLP(\n",
|
|||
|
|
" (c_fc): Conv1D()\n",
|
|||
|
|
" (c_proj): Conv1D()\n",
|
|||
|
|
" (act): NewGELUActivation()\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (9): GPT2Block(\n",
|
|||
|
|
" (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" (attn): GPT2Attention(\n",
|
|||
|
|
" (c_attn): Conv1D()\n",
|
|||
|
|
" (c_proj): Conv1D()\n",
|
|||
|
|
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" (mlp): GPT2MLP(\n",
|
|||
|
|
" (c_fc): Conv1D()\n",
|
|||
|
|
" (c_proj): Conv1D()\n",
|
|||
|
|
" (act): NewGELUActivation()\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (10): GPT2Block(\n",
|
|||
|
|
" (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" (attn): GPT2Attention(\n",
|
|||
|
|
" (c_attn): Conv1D()\n",
|
|||
|
|
" (c_proj): Conv1D()\n",
|
|||
|
|
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" (mlp): GPT2MLP(\n",
|
|||
|
|
" (c_fc): Conv1D()\n",
|
|||
|
|
" (c_proj): Conv1D()\n",
|
|||
|
|
" (act): NewGELUActivation()\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (11): GPT2Block(\n",
|
|||
|
|
" (ln_1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" (attn): GPT2Attention(\n",
|
|||
|
|
" (c_attn): Conv1D()\n",
|
|||
|
|
" (c_proj): Conv1D()\n",
|
|||
|
|
" (attn_dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" (resid_dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (ln_2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" (mlp): GPT2MLP(\n",
|
|||
|
|
" (c_fc): Conv1D()\n",
|
|||
|
|
" (c_proj): Conv1D()\n",
|
|||
|
|
" (act): NewGELUActivation()\n",
|
|||
|
|
" (dropout): Dropout(p=0.1, inplace=False)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (ln_f): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
|
|||
|
|
" )\n",
|
|||
|
|
" (score): Linear(in_features=768, out_features=2, bias=False)\n",
|
|||
|
|
")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 13
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"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",
|
|||
|
|
"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",
|
|||
|
|
" 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",
|
|||
|
|
" avg_val_accuracy = total_eval_accuracy / len(validation_dataloader)\n",
|
|||
|
|
" print(\" Accuracy: {0:.2f}\".format(avg_val_accuracy))\n",
|
|||
|
|
"\n",
|
|||
|
|
" avg_val_loss = total_eval_loss / len(validation_dataloader)\n",
|
|||
|
|
" validation_time = format_time(time.time() - t0)\n",
|
|||
|
|
" \n",
|
|||
|
|
" print(\" Validation Loss: {0:.2f}\".format(avg_val_loss))\n",
|
|||
|
|
" print(\" Validation took: {:}\".format(validation_time))\n",
|
|||
|
|
"\n",
|
|||
|
|
" training_stats.append(\n",
|
|||
|
|
" {\n",
|
|||
|
|
" 'epoch': epoch_i + 1,\n",
|
|||
|
|
" 'Training Loss': avg_train_loss,\n",
|
|||
|
|
" 'Valid. Loss': avg_val_loss,\n",
|
|||
|
|
" 'Valid. Accur.': avg_val_accuracy,\n",
|
|||
|
|
" 'Training Time': training_time,\n",
|
|||
|
|
" 'Validation Time': validation_time\n",
|
|||
|
|
" }\n",
|
|||
|
|
" )\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"\")\n",
|
|||
|
|
"print(\"Training complete!\")\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"Total training took {:} (h:mm:ss)\".format(format_time(time.time()-total_t0)))"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "hnq-2iztdYie",
|
|||
|
|
"outputId": "f2cf6703-9ab8-4dbf-e5d4-22f899a28776"
|
|||
|
|
},
|
|||
|
|
"execution_count": 16,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"\n",
|
|||
|
|
"======== Epoch 1 / 4 ========\n",
|
|||
|
|
"Training...\n",
|
|||
|
|
" Batch 40 of 515. Elapsed: 0:00:19.\n",
|
|||
|
|
" Batch 80 of 515. Elapsed: 0:00:35.\n",
|
|||
|
|
" Batch 120 of 515. Elapsed: 0:00:52.\n",
|
|||
|
|
" Batch 160 of 515. Elapsed: 0:01:08.\n",
|
|||
|
|
" Batch 200 of 515. Elapsed: 0:01:25.\n",
|
|||
|
|
" Batch 240 of 515. Elapsed: 0:01:42.\n",
|
|||
|
|
" Batch 280 of 515. Elapsed: 0:01:58.\n",
|
|||
|
|
" Batch 320 of 515. Elapsed: 0:02:15.\n",
|
|||
|
|
" Batch 360 of 515. Elapsed: 0:02:32.\n",
|
|||
|
|
" Batch 400 of 515. Elapsed: 0:02:49.\n",
|
|||
|
|
" Batch 440 of 515. Elapsed: 0:03:06.\n",
|
|||
|
|
" Batch 480 of 515. Elapsed: 0:03:24.\n",
|
|||
|
|
"\n",
|
|||
|
|
" Average training loss: 0.14\n",
|
|||
|
|
" Training epcoh took: 0:03:38\n",
|
|||
|
|
"\n",
|
|||
|
|
"Running Validation...\n",
|
|||
|
|
" Accuracy: 0.97\n",
|
|||
|
|
" Validation Loss: 0.21\n",
|
|||
|
|
" Validation took: 0:00:08\n",
|
|||
|
|
"\n",
|
|||
|
|
"======== Epoch 2 / 4 ========\n",
|
|||
|
|
"Training...\n",
|
|||
|
|
" Batch 40 of 515. Elapsed: 0:00:17.\n",
|
|||
|
|
" Batch 80 of 515. Elapsed: 0:00:35.\n",
|
|||
|
|
" Batch 120 of 515. Elapsed: 0:00:52.\n",
|
|||
|
|
" Batch 160 of 515. Elapsed: 0:01:09.\n",
|
|||
|
|
" Batch 200 of 515. Elapsed: 0:01:27.\n",
|
|||
|
|
" Batch 240 of 515. Elapsed: 0:01:44.\n",
|
|||
|
|
" Batch 280 of 515. Elapsed: 0:02:01.\n",
|
|||
|
|
" Batch 320 of 515. Elapsed: 0:02:19.\n",
|
|||
|
|
" Batch 360 of 515. Elapsed: 0:02:36.\n",
|
|||
|
|
" Batch 400 of 515. Elapsed: 0:02:54.\n",
|
|||
|
|
" Batch 440 of 515. Elapsed: 0:03:11.\n",
|
|||
|
|
" Batch 480 of 515. Elapsed: 0:03:29.\n",
|
|||
|
|
"\n",
|
|||
|
|
" Average training loss: 0.04\n",
|
|||
|
|
" Training epcoh took: 0:03:44\n",
|
|||
|
|
"\n",
|
|||
|
|
"Running Validation...\n",
|
|||
|
|
" Accuracy: 0.97\n",
|
|||
|
|
" Validation Loss: 0.19\n",
|
|||
|
|
" Validation took: 0:00:08\n",
|
|||
|
|
"\n",
|
|||
|
|
"======== Epoch 3 / 4 ========\n",
|
|||
|
|
"Training...\n",
|
|||
|
|
" Batch 40 of 515. Elapsed: 0:00:17.\n",
|
|||
|
|
" Batch 80 of 515. Elapsed: 0:00:35.\n",
|
|||
|
|
" Batch 120 of 515. Elapsed: 0:00:52.\n",
|
|||
|
|
" Batch 160 of 515. Elapsed: 0:01:10.\n",
|
|||
|
|
" Batch 200 of 515. Elapsed: 0:01:27.\n",
|
|||
|
|
" Batch 240 of 515. Elapsed: 0:01:45.\n",
|
|||
|
|
" Batch 280 of 515. Elapsed: 0:02:02.\n",
|
|||
|
|
" Batch 320 of 515. Elapsed: 0:02:20.\n",
|
|||
|
|
" Batch 360 of 515. Elapsed: 0:02:37.\n",
|
|||
|
|
" Batch 400 of 515. Elapsed: 0:02:55.\n",
|
|||
|
|
" Batch 440 of 515. Elapsed: 0:03:12.\n",
|
|||
|
|
" Batch 480 of 515. Elapsed: 0:03:30.\n",
|
|||
|
|
"\n",
|
|||
|
|
" Average training loss: 0.03\n",
|
|||
|
|
" Training epcoh took: 0:03:45\n",
|
|||
|
|
"\n",
|
|||
|
|
"Running Validation...\n",
|
|||
|
|
" Accuracy: 0.97\n",
|
|||
|
|
" Validation Loss: 0.16\n",
|
|||
|
|
" Validation took: 0:00:08\n",
|
|||
|
|
"\n",
|
|||
|
|
"======== Epoch 4 / 4 ========\n",
|
|||
|
|
"Training...\n",
|
|||
|
|
" Batch 40 of 515. Elapsed: 0:00:17.\n",
|
|||
|
|
" Batch 80 of 515. Elapsed: 0:00:35.\n",
|
|||
|
|
" Batch 120 of 515. Elapsed: 0:00:52.\n",
|
|||
|
|
" Batch 160 of 515. Elapsed: 0:01:10.\n",
|
|||
|
|
" Batch 200 of 515. Elapsed: 0:01:27.\n",
|
|||
|
|
" Batch 240 of 515. Elapsed: 0:01:45.\n",
|
|||
|
|
" Batch 280 of 515. Elapsed: 0:02:02.\n",
|
|||
|
|
" Batch 320 of 515. Elapsed: 0:02:20.\n",
|
|||
|
|
" Batch 360 of 515. Elapsed: 0:02:37.\n",
|
|||
|
|
" Batch 400 of 515. Elapsed: 0:02:55.\n",
|
|||
|
|
" Batch 440 of 515. Elapsed: 0:03:12.\n",
|
|||
|
|
" Batch 480 of 515. Elapsed: 0:03:30.\n",
|
|||
|
|
"\n",
|
|||
|
|
" Average training loss: 0.01\n",
|
|||
|
|
" Training epcoh took: 0:03:45\n",
|
|||
|
|
"\n",
|
|||
|
|
"Running Validation...\n",
|
|||
|
|
" Accuracy: 0.98\n",
|
|||
|
|
" Validation Loss: 0.11\n",
|
|||
|
|
" Validation took: 0:00:08\n",
|
|||
|
|
"\n",
|
|||
|
|
"Training complete!\n",
|
|||
|
|
"Total training took 0:15:24 (h:mm:ss)\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Train summary"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "z3nngo5DgZe4"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"import pandas as pd\n",
|
|||
|
|
"\n",
|
|||
|
|
"pd.set_option('precision', 2)\n",
|
|||
|
|
"df_stats = pd.DataFrame(data=training_stats)\n",
|
|||
|
|
"\n",
|
|||
|
|
"df_stats = df_stats.set_index('epoch')\n",
|
|||
|
|
"df_stats"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 206
|
|||
|
|
},
|
|||
|
|
"id": "qVSGSZ5-gbnV",
|
|||
|
|
"outputId": "b6e5d689-6748-4e0d-a43d-0484de05129d"
|
|||
|
|
},
|
|||
|
|
"execution_count": 17,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
" Training Loss Valid. Loss Valid. Accur. Training Time Validation Time\n",
|
|||
|
|
"epoch \n",
|
|||
|
|
"1 0.14 0.21 0.97 0:03:38 0:00:08\n",
|
|||
|
|
"2 0.04 0.19 0.97 0:03:44 0:00:08\n",
|
|||
|
|
"3 0.03 0.16 0.97 0:03:45 0:00:08\n",
|
|||
|
|
"4 0.01 0.11 0.98 0:03:45 0:00:08"
|
|||
|
|
],
|
|||
|
|
"text/html": [
|
|||
|
|
"\n",
|
|||
|
|
" <div id=\"df-6c6be55d-42e7-4ea4-9c6a-7e650f43571b\">\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>0.14</td>\n",
|
|||
|
|
" <td>0.21</td>\n",
|
|||
|
|
" <td>0.97</td>\n",
|
|||
|
|
" <td>0:03:38</td>\n",
|
|||
|
|
" <td>0:00:08</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>0.04</td>\n",
|
|||
|
|
" <td>0.19</td>\n",
|
|||
|
|
" <td>0.97</td>\n",
|
|||
|
|
" <td>0:03:44</td>\n",
|
|||
|
|
" <td>0:00:08</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>0.03</td>\n",
|
|||
|
|
" <td>0.16</td>\n",
|
|||
|
|
" <td>0.97</td>\n",
|
|||
|
|
" <td>0:03:45</td>\n",
|
|||
|
|
" <td>0:00:08</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>0.01</td>\n",
|
|||
|
|
" <td>0.11</td>\n",
|
|||
|
|
" <td>0.98</td>\n",
|
|||
|
|
" <td>0:03:45</td>\n",
|
|||
|
|
" <td>0:00:08</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>\n",
|
|||
|
|
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6c6be55d-42e7-4ea4-9c6a-7e650f43571b')\"\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-6c6be55d-42e7-4ea4-9c6a-7e650f43571b 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-6c6be55d-42e7-4ea4-9c6a-7e650f43571b');\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": "qhDALEePhHp1",
|
|||
|
|
"outputId": "ddcab2bb-da52-4647-8d04-167993f6c98f"
|
|||
|
|
},
|
|||
|
|
"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": "7gwWvjFwhJen"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"prediction_dataloader = DataLoader(\n",
|
|||
|
|
" test_dataset,\n",
|
|||
|
|
" sampler = SequentialSampler(test_dataset),\n",
|
|||
|
|
" batch_size = batch_size\n",
|
|||
|
|
" )"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "du6qCdHyhMms"
|
|||
|
|
},
|
|||
|
|
"execution_count": 19,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Evaluate on test dataset"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "n9E84sH2hOt7"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"print('Predicting labels for {:,} test sentences...'.format(len(test_dataset)))\n",
|
|||
|
|
"\n",
|
|||
|
|
"model.eval()\n",
|
|||
|
|
"predictions , true_labels = [], []\n",
|
|||
|
|
"\n",
|
|||
|
|
"for batch in prediction_dataloader:\n",
|
|||
|
|
" batch = tuple(t.to(device) for t in batch)\n",
|
|||
|
|
" \n",
|
|||
|
|
" b_input_ids, b_input_mask, b_labels = batch\n",
|
|||
|
|
" \n",
|
|||
|
|
" with torch.no_grad():\n",
|
|||
|
|
" outputs = model(b_input_ids, token_type_ids=None, \n",
|
|||
|
|
" attention_mask=b_input_mask)\n",
|
|||
|
|
"\n",
|
|||
|
|
" logits = outputs['logits']\n",
|
|||
|
|
"\n",
|
|||
|
|
" logits = logits.detach().cpu().numpy()\n",
|
|||
|
|
" label_ids = b_labels.to('cpu').numpy()\n",
|
|||
|
|
"\n",
|
|||
|
|
" predictions.append(logits)\n",
|
|||
|
|
" true_labels.append(label_ids)\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(' DONE.')"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "s3nFSXgbhRs1",
|
|||
|
|
"outputId": "39a16e42-8d7e-4e31-95f1-e29118ce62f3"
|
|||
|
|
},
|
|||
|
|
"execution_count": 20,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Predicting labels for 1,000 test sentences...\n",
|
|||
|
|
" DONE.\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"results_ok = 0\n",
|
|||
|
|
"results_false = 0\n",
|
|||
|
|
"for idx, true_labels_batch in enumerate(true_labels):\n",
|
|||
|
|
" predictions_i = np.argmax(predictions[idx], axis=1).flatten()\n",
|
|||
|
|
" for bidx, true_label in enumerate(true_labels_batch):\n",
|
|||
|
|
" if true_label == predictions_i[bidx]:\n",
|
|||
|
|
" results_ok += 1\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" results_false += 1\n",
|
|||
|
|
"\n",
|
|||
|
|
"print(\"Correct predictions: {}, incorrect results: {}, accuracy: {}\".format(results_ok, results_false, float(results_ok) / (results_ok + results_false)))"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "eNMYIt7RhWYM",
|
|||
|
|
"outputId": "7257f066-6539-4e42-d0ae-e6c5609f1812"
|
|||
|
|
},
|
|||
|
|
"execution_count": 21,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Correct predictions: 990, incorrect results: 10, accuracy: 0.99\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# MCC Score"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "SwHJwpqKhZ51"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"from sklearn.metrics import matthews_corrcoef\n",
|
|||
|
|
"\n",
|
|||
|
|
"matthews_set = []\n",
|
|||
|
|
"print('Calculating Matthews Corr. Coef. for each batch...')\n",
|
|||
|
|
"\n",
|
|||
|
|
"for i in range(len(true_labels)):\n",
|
|||
|
|
" pred_labels_i = np.argmax(predictions[i], axis=1).flatten()\n",
|
|||
|
|
" \n",
|
|||
|
|
" matthews = matthews_corrcoef(true_labels[i], pred_labels_i) \n",
|
|||
|
|
" matthews_set.append(matthews)"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "oqfHjUXThb2J",
|
|||
|
|
"outputId": "2bbfcaeb-5ea8-498e-a5a0-8f2fac83feea"
|
|||
|
|
},
|
|||
|
|
"execution_count": 22,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Calculating Matthews Corr. Coef. for each batch...\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"ax = sns.barplot(x=list(range(len(matthews_set))), y=matthews_set, ci=None)\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.title('MCC Score per Batch')\n",
|
|||
|
|
"plt.ylabel('MCC Score (-1 to +1)')\n",
|
|||
|
|
"plt.xlabel('Batch #')\n",
|
|||
|
|
"\n",
|
|||
|
|
"plt.show()"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/",
|
|||
|
|
"height": 427
|
|||
|
|
},
|
|||
|
|
"id": "JJoRzvr0hePf",
|
|||
|
|
"outputId": "ebc78102-65e6-4847-d3c0-d3825870dc78"
|
|||
|
|
},
|
|||
|
|
"execution_count": 23,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "display_data",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"<Figure size 864x432 with 1 Axes>"
|
|||
|
|
],
|
|||
|
|
"image/png": "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
|
|||
|
|
},
|
|||
|
|
"metadata": {}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"flat_predictions = np.concatenate(predictions, axis=0)\n",
|
|||
|
|
"flat_predictions = np.argmax(flat_predictions, axis=1).flatten()\n",
|
|||
|
|
"\n",
|
|||
|
|
"flat_true_labels = np.concatenate(true_labels, axis=0)\n",
|
|||
|
|
"\n",
|
|||
|
|
"mcc = matthews_corrcoef(flat_true_labels, flat_predictions)\n",
|
|||
|
|
"\n",
|
|||
|
|
"print('Total MCC: %.3f' % mcc)"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "8XER3sOFhfny",
|
|||
|
|
"outputId": "77ec6114-8ab3-4abd-c7b7-de95528a2bef"
|
|||
|
|
},
|
|||
|
|
"execution_count": 24,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Total MCC: 0.960\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Save model"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "ZTd3f1yKhhkP"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"from google.colab import drive\n",
|
|||
|
|
"\n",
|
|||
|
|
"drive.mount('/content/gdrive/', force_remount=True)\n",
|
|||
|
|
"\n",
|
|||
|
|
"output_dir = '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/GPT2_Model'\n",
|
|||
|
|
"print(\"Saving model to %s\" % output_dir)\n",
|
|||
|
|
"\n",
|
|||
|
|
"model_to_save = model.module if hasattr(model, 'module') else model\n",
|
|||
|
|
"model_to_save.save_pretrained(output_dir)\n",
|
|||
|
|
"tokenizer.save_pretrained(output_dir)"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "gqSUWqCqhizx",
|
|||
|
|
"outputId": "76d1febd-031d-456a-b108-7b664b2b5729"
|
|||
|
|
},
|
|||
|
|
"execution_count": 25,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Mounted at /content/gdrive/\n",
|
|||
|
|
"Saving model to /content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/GPT2_Model\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"('/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/GPT2_Model/tokenizer_config.json',\n",
|
|||
|
|
" '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/GPT2_Model/special_tokens_map.json',\n",
|
|||
|
|
" '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/GPT2_Model/vocab.json',\n",
|
|||
|
|
" '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/GPT2_Model/merges.txt',\n",
|
|||
|
|
" '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/GPT2_Model/added_tokens.json')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 25
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Bibliografia\n",
|
|||
|
|
"- https://gmihaila.github.io/tutorial_notebooks/gpt2_finetune_classification/\n",
|
|||
|
|
"- https://mccormickml.com/2019/07/22/BERT-fine-tuning/#a1-saving--loading-fine-tuned-model"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "Er-thm7dkbIW"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
}
|