5417 lines
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5417 lines
278 KiB
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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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"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 BertTokenizer\n",
|
|||
|
|
"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 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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"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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|
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"d0e5525f045f409287c8873153ef495c",
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"84c8cdd28bb74cdca76b1aba43a326dc",
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"cae316c7fd2b42b4901fe22cc07f6a28",
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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",
|
|||
|
|
"text": [
|
|||
|
|
"Dataset sms_spam downloaded and prepared to /root/.cache/huggingface/datasets/sms_spam/plain_text/1.0.0/53f051d3b5f62d99d61792c91acefe4f1577ad3e4c216fb0ad39e30b9f20019c. Subsequent calls will reuse this data.\n"
|
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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": [
|
|||
|
|
"dataset['train'][0]"
|
|||
|
|
],
|
|||
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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": "a36bc069-6dc5-47bb-eae8-302dd8dd15dd"
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},
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"execution_count": 4,
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"outputs": [
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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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"cell_type": "markdown",
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"source": [
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|||
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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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|
{
|
|||
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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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|
"dd1ec83063aa42faacf6e996dee95a58",
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|
"563eb31845224d4093e9dc96cf57c445",
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|
"7ea724412ddf4fdaaf9c76f19c39ab34",
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|
"b1d74065386d454788fb573b7a01d73a",
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|
"16d7dfccfd0c4801ba14eaa3640ea231",
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|
"91a0abd03d494b2699e8eb645ed7eda7",
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"acfe963e793840f5af98d016c3a5be4d",
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"12eecded77aa4c0b8270958c429c5e33",
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"e7eb4aa69ede435e911ccb4f6bb27eca",
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"7df4f48608fc4f3185771b93105584dc",
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"1e5ffcf81cc649c3b9beacb8a7ab70c8",
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"1e464642d1a046eba08a6b5b94dfd060",
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"02f6d5269ce6403b93301e1cc0810c2e",
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"93d7c900c84b488ba7fc0e801e8db5a7",
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"a0bc131511d941189e834a4c6eab91ad",
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"585307a3684a47cc8f0bd1f92b682373",
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"b2e8a810e8c54e6b99ef5f9597a20077",
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|
"c61939fef83c4cb987192a3aaccc64fe",
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"412fe82e08214f89804c8d68a599ac78",
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"d0b28111954e4a2abba91a096c19f06c",
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"d5b8ded6476744a3b935d167d44b3b56",
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"1f5be997bd8641afbd3e0534387fe378",
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"99e4648ab3bb4068afabf5bb441dbd43",
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"52a54be01e8f48a180e966021b38a629",
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"c8c5f72bee734aeca1019c11bedf1b3b",
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"e302c57ba0a14ba8bed6825155dac372",
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"a4c5260f6e89479088a3e22e48e2dfe3",
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"38a5be06cf344b1ba233bcb4b380ef40",
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"aebaeada4ced48058c2aeeb825b43d12",
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"67dd29a08bb549dfb8515bbb55a83c63",
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"ca258a0856da4668a6efaae834984f1a",
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"5b334b7e24224d0291c68a765bee7a83",
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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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"version_major": 2,
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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/"
|
|||
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|
},
|
|||
|
|
"id": "ZxigrpcQdWCF",
|
|||
|
|
"outputId": "6db555e8-0670-48e0-e2af-f89c7e49a011"
|
|||
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|
},
|
|||
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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/",
|
|||
|
|
"height": 34
|
|||
|
|
},
|
|||
|
|
"id": "cmUVPrQYez3J",
|
|||
|
|
"outputId": "511e415a-8544-441f-a650-89d5cb4b4115"
|
|||
|
|
},
|
|||
|
|
"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": "32ddbde0-eced-48b1-a78b-8d835236143f"
|
|||
|
|
},
|
|||
|
|
"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\n",
|
|||
|
|
"Class balance ratio should be similar to base dataset ratio."
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "Z6cC0YjAhmw_"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"def check_class_balance(dataset):\n",
|
|||
|
|
" spam_count = 0.0\n",
|
|||
|
|
" not_spam_count = 0.0\n",
|
|||
|
|
" for row in dataset:\n",
|
|||
|
|
" if row[2].item() == 1:\n",
|
|||
|
|
" spam_count += 1.0\n",
|
|||
|
|
" else:\n",
|
|||
|
|
" not_spam_count += 1.0\n",
|
|||
|
|
" return spam_count / not_spam_count "
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "QrEm9LLfvaDq"
|
|||
|
|
},
|
|||
|
|
"execution_count": 16,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"dataset = TensorDataset(input_ids, attention_masks, labels)\n",
|
|||
|
|
"print(\"Spam to not spam messages ratio: {}\\n\".format(check_class_balance(dataset)))\n",
|
|||
|
|
"\n",
|
|||
|
|
"test_size = 1000\n",
|
|||
|
|
"dataset_len = len(dataset)\n",
|
|||
|
|
"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(\"Ratio: {}\\n\".format(check_class_balance(test_dataset)))\n",
|
|||
|
|
"print('{:>5,} training samples'.format(train_size))\n",
|
|||
|
|
"print(\"Ratio: {}\\n\".format(check_class_balance(train_dataset)))\n",
|
|||
|
|
"print('{:>5,} validation samples'.format(val_size))\n",
|
|||
|
|
"print(\"Ratio: {}\\n\".format(check_class_balance(val_dataset)))"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "vH3yXhA0hT3n",
|
|||
|
|
"outputId": "4fc01e6d-5f37-4fd0-f7d6-a6ba43e6875d"
|
|||
|
|
},
|
|||
|
|
"execution_count": 23,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Spam to not spam messages ratio: 0.15475450590428838\n",
|
|||
|
|
"\n",
|
|||
|
|
"1,000 test samples\n",
|
|||
|
|
"Ratio: 0.1792452830188679\n",
|
|||
|
|
"\n",
|
|||
|
|
"4,116 training samples\n",
|
|||
|
|
"Ratio: 0.15100671140939598\n",
|
|||
|
|
"\n",
|
|||
|
|
" 458 validation samples\n",
|
|||
|
|
"Ratio: 0.13647642679900746\n",
|
|||
|
|
"\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "markdown",
|
|||
|
|
"source": [
|
|||
|
|
"# Create train and validation loaders"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "z1hVsejihpO2"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"batch_size = 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": 24,
|
|||
|
|
"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": "24abe05a-2dd4-46ae-90ed-b0b775aecfc2"
|
|||
|
|
},
|
|||
|
|
"execution_count": 25,
|
|||
|
|
"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": [
|
|||
|
|
"# Custom BERT for classification\n",
|
|||
|
|
"Compared to default BertForSequenceClassification - additional linear layer.\n",
|
|||
|
|
"https://github.com/huggingface/transformers/blob/bd469c40659ce76c81f69c7726759d249b4aef49/src/transformers/models/bert/modeling_bert.py#L1506"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "o-YrojT-iIfY"
|
|||
|
|
}
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"from transformers import BertForSequenceClassification, BertConfig, BertModel\n",
|
|||
|
|
"from torch import nn"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"id": "GlTdPmbxtMPI"
|
|||
|
|
},
|
|||
|
|
"execution_count": 27,
|
|||
|
|
"outputs": []
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"source": [
|
|||
|
|
"# in original model - no custom head just linear layer\n",
|
|||
|
|
"# self.classifier = nn.Linear(config.hidden_size, config.num_labels)\n",
|
|||
|
|
"\n",
|
|||
|
|
"class BertClassificationHeadCustom(nn.Module):\n",
|
|||
|
|
" def __init__(self, config):\n",
|
|||
|
|
" super().__init__()\n",
|
|||
|
|
" self.dense_1 = nn.Linear(config.hidden_size, config.hidden_size)\n",
|
|||
|
|
" self.dense_2 = nn.Linear(config.hidden_size, config.num_labels)\n",
|
|||
|
|
"\n",
|
|||
|
|
" def forward(self, x):\n",
|
|||
|
|
" x = self.dense_1(x)\n",
|
|||
|
|
" x = torch.relu(x)\n",
|
|||
|
|
"\n",
|
|||
|
|
" x = self.dense_2(x)\n",
|
|||
|
|
" x = torch.relu(x)\n",
|
|||
|
|
" return x\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"class BertForSequenceClassificationCustom(BertForSequenceClassification):\n",
|
|||
|
|
" def __init__(self, config):\n",
|
|||
|
|
" super().__init__(config)\n",
|
|||
|
|
" self.num_labels = config.num_labels\n",
|
|||
|
|
" self.config = config\n",
|
|||
|
|
"\n",
|
|||
|
|
" self.bert = BertModel(config)\n",
|
|||
|
|
" classifier_dropout = (\n",
|
|||
|
|
" config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob\n",
|
|||
|
|
" )\n",
|
|||
|
|
" self.dropout = nn.Dropout(classifier_dropout)\n",
|
|||
|
|
" self.classifier = BertClassificationHeadCustom(config)\n",
|
|||
|
|
"\n",
|
|||
|
|
" # Initialize weights and apply final processing\n",
|
|||
|
|
" self.post_init()\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"\n",
|
|||
|
|
"model = BertForSequenceClassificationCustom.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": [
|
|||
|
|
"d92c1e0371f44bf1a09ab322f66e37af",
|
|||
|
|
"9fb44ba6139b41849d40fb5ac20301da",
|
|||
|
|
"81efef51b9d245bba85c978f34b4b945",
|
|||
|
|
"4db1c74d5d5b4ea3a9bf298a30f69dfe",
|
|||
|
|
"9c1441d4a6594e1894af6554ea357453",
|
|||
|
|
"90fd9ee5752a498bb1fe1e85469c86f5",
|
|||
|
|
"ba8f4bb285154ca08194c4dc912b0d8f",
|
|||
|
|
"dd2a99f30f4e403e89cde06b1acab3d4",
|
|||
|
|
"018f52dd2214404c85a53593f0e2ebc3",
|
|||
|
|
"d65dd677908d4e6c873291c59d94b729",
|
|||
|
|
"5a2347b2bca94f798434ec43fbd41028"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"id": "sIP3VGZmiK9s",
|
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|
|
"outputId": "bc2ef8ba-1a9d-4d83-fc07-82d09d188630"
|
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},
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"execution_count": 28,
|
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"outputs": [
|
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{
|
|||
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"output_type": "display_data",
|
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"data": {
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"text/plain": [
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"Downloading (…)\"pytorch_model.bin\";: 0%| | 0.00/440M [00:00<?, ?B/s]"
|
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],
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"application/vnd.jupyter.widget-view+json": {
|
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"version_major": 2,
|
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"version_minor": 0,
|
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|
"model_id": "d92c1e0371f44bf1a09ab322f66e37af"
|
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}
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},
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"metadata": {}
|
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|
},
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{
|
|||
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"output_type": "stream",
|
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|
"name": "stderr",
|
|||
|
|
"text": [
|
|||
|
|
"Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassificationCustom: ['cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight']\n",
|
|||
|
|
"- This IS expected if you are initializing BertForSequenceClassificationCustom 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 BertForSequenceClassificationCustom 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 BertForSequenceClassificationCustom were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.dense_1.weight', 'classifier.dense_2.weight', 'classifier.dense_2.bias', 'classifier.dense_1.bias']\n",
|
|||
|
|
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
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{
|
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"output_type": "execute_result",
|
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"data": {
|
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"text/plain": [
|
|||
|
|
"BertForSequenceClassificationCustom(\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): BertClassificationHeadCustom(\n",
|
|||
|
|
" (dense_1): Linear(in_features=768, out_features=768, bias=True)\n",
|
|||
|
|
" (dense_2): Linear(in_features=768, out_features=2, bias=True)\n",
|
|||
|
|
" )\n",
|
|||
|
|
")"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 28
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"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": 29,
|
|||
|
|
"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": 30,
|
|||
|
|
"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": "ad4bce2c-b443-4fe7-fdf2-99d678821d46"
|
|||
|
|
},
|
|||
|
|
"execution_count": 31,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"\n",
|
|||
|
|
"======== Epoch 1 / 4 ========\n",
|
|||
|
|
"Training...\n",
|
|||
|
|
" Batch 40 of 129. Elapsed: 0:00:48.\n",
|
|||
|
|
" Batch 80 of 129. Elapsed: 0:01:35.\n",
|
|||
|
|
" Batch 120 of 129. Elapsed: 0:02:23.\n",
|
|||
|
|
"\n",
|
|||
|
|
" Average training loss: 0.13\n",
|
|||
|
|
" Training epcoh took: 0:02:34\n",
|
|||
|
|
"\n",
|
|||
|
|
"Running Validation...\n",
|
|||
|
|
" Accuracy: 0.98\n",
|
|||
|
|
" Validation Loss: 0.08\n",
|
|||
|
|
" Validation took: 0:00:06\n",
|
|||
|
|
"\n",
|
|||
|
|
"======== Epoch 2 / 4 ========\n",
|
|||
|
|
"Training...\n",
|
|||
|
|
" Batch 40 of 129. Elapsed: 0:00:49.\n",
|
|||
|
|
" Batch 80 of 129. Elapsed: 0:01:38.\n",
|
|||
|
|
" Batch 120 of 129. Elapsed: 0:02:27.\n",
|
|||
|
|
"\n",
|
|||
|
|
" Average training loss: 0.03\n",
|
|||
|
|
" Training epcoh took: 0:02:38\n",
|
|||
|
|
"\n",
|
|||
|
|
"Running Validation...\n",
|
|||
|
|
" Accuracy: 0.99\n",
|
|||
|
|
" Validation Loss: 0.06\n",
|
|||
|
|
" Validation took: 0:00:06\n",
|
|||
|
|
"\n",
|
|||
|
|
"======== Epoch 3 / 4 ========\n",
|
|||
|
|
"Training...\n",
|
|||
|
|
" Batch 40 of 129. Elapsed: 0:00:49.\n",
|
|||
|
|
" Batch 80 of 129. Elapsed: 0:01:37.\n",
|
|||
|
|
" Batch 120 of 129. Elapsed: 0:02:26.\n",
|
|||
|
|
"\n",
|
|||
|
|
" Average training loss: 0.01\n",
|
|||
|
|
" Training epcoh took: 0:02:37\n",
|
|||
|
|
"\n",
|
|||
|
|
"Running Validation...\n",
|
|||
|
|
" Accuracy: 0.99\n",
|
|||
|
|
" Validation Loss: 0.06\n",
|
|||
|
|
" Validation took: 0:00:06\n",
|
|||
|
|
"\n",
|
|||
|
|
"======== Epoch 4 / 4 ========\n",
|
|||
|
|
"Training...\n",
|
|||
|
|
" Batch 40 of 129. Elapsed: 0:00:49.\n",
|
|||
|
|
" Batch 80 of 129. Elapsed: 0:01:37.\n",
|
|||
|
|
" Batch 120 of 129. Elapsed: 0:02:26.\n",
|
|||
|
|
"\n",
|
|||
|
|
" Average training loss: 0.01\n",
|
|||
|
|
" Training epcoh took: 0:02:36\n",
|
|||
|
|
"\n",
|
|||
|
|
"Running Validation...\n",
|
|||
|
|
" Accuracy: 0.99\n",
|
|||
|
|
" Validation Loss: 0.06\n",
|
|||
|
|
" Validation took: 0:00:06\n",
|
|||
|
|
"\n",
|
|||
|
|
"Training complete!\n",
|
|||
|
|
"Total training took 0:10:50 (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": 204
|
|||
|
|
},
|
|||
|
|
"id": "w4ov2mClrLGW",
|
|||
|
|
"outputId": "b574eb2f-90ff-47b2-ac8f-99682c9b938d"
|
|||
|
|
},
|
|||
|
|
"execution_count": 32,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
" Training Loss Valid. Loss Valid. Accur. Training Time Validation Time\n",
|
|||
|
|
"epoch \n",
|
|||
|
|
"1 1.26e-01 0.08 0.98 0:02:34 0:00:06\n",
|
|||
|
|
"2 2.58e-02 0.06 0.99 0:02:38 0:00:06\n",
|
|||
|
|
"3 1.26e-02 0.06 0.99 0:02:37 0:00:06\n",
|
|||
|
|
"4 9.73e-03 0.06 0.99 0:02:36 0:00:06"
|
|||
|
|
],
|
|||
|
|
"text/html": [
|
|||
|
|
"\n",
|
|||
|
|
" <div id=\"df-e3d8028e-4cc3-459b-812f-23955d700883\">\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>1.26e-01</td>\n",
|
|||
|
|
" <td>0.08</td>\n",
|
|||
|
|
" <td>0.98</td>\n",
|
|||
|
|
" <td>0:02:34</td>\n",
|
|||
|
|
" <td>0:00:06</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>2</th>\n",
|
|||
|
|
" <td>2.58e-02</td>\n",
|
|||
|
|
" <td>0.06</td>\n",
|
|||
|
|
" <td>0.99</td>\n",
|
|||
|
|
" <td>0:02:38</td>\n",
|
|||
|
|
" <td>0:00:06</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>3</th>\n",
|
|||
|
|
" <td>1.26e-02</td>\n",
|
|||
|
|
" <td>0.06</td>\n",
|
|||
|
|
" <td>0.99</td>\n",
|
|||
|
|
" <td>0:02:37</td>\n",
|
|||
|
|
" <td>0:00:06</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" <tr>\n",
|
|||
|
|
" <th>4</th>\n",
|
|||
|
|
" <td>9.73e-03</td>\n",
|
|||
|
|
" <td>0.06</td>\n",
|
|||
|
|
" <td>0.99</td>\n",
|
|||
|
|
" <td>0:02:36</td>\n",
|
|||
|
|
" <td>0:00:06</td>\n",
|
|||
|
|
" </tr>\n",
|
|||
|
|
" </tbody>\n",
|
|||
|
|
"</table>\n",
|
|||
|
|
"</div>\n",
|
|||
|
|
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e3d8028e-4cc3-459b-812f-23955d700883')\"\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-e3d8028e-4cc3-459b-812f-23955d700883 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-e3d8028e-4cc3-459b-812f-23955d700883');\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": 32
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"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": "bb270a48-8d19-4854-a074-2845da4ee513"
|
|||
|
|
},
|
|||
|
|
"execution_count": 33,
|
|||
|
|
"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": 34,
|
|||
|
|
"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": "6f187c3f-7a21-4cb8-b2c1-b738fdcf9cf9"
|
|||
|
|
},
|
|||
|
|
"execution_count": 35,
|
|||
|
|
"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": "6e8909c3-9337-4de4-f268-c1fa957d3e81"
|
|||
|
|
},
|
|||
|
|
"execution_count": 36,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Correct predictions: 995, incorrect results: 5, accuracy: 0.995\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": "a56f6011-7da3-43e0-98bb-d3193036d5e0"
|
|||
|
|
},
|
|||
|
|
"execution_count": 37,
|
|||
|
|
"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": "8922ba13-2295-481d-9e14-2b75fed302da"
|
|||
|
|
},
|
|||
|
|
"execution_count": 38,
|
|||
|
|
"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",
|
|||
|
|
"\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": "692cc582-19fc-468e-8098-f2287a45b1d2"
|
|||
|
|
},
|
|||
|
|
"execution_count": 39,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Total MCC: 0.981\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_custom_model'\n",
|
|||
|
|
"print(\"Saving model to %s\" % output_dir)\n",
|
|||
|
|
"\n",
|
|||
|
|
"model_to_save = model.module if hasattr(model, 'module') else model\n",
|
|||
|
|
"model_to_save.save_pretrained(output_dir)\n",
|
|||
|
|
"tokenizer.save_pretrained(output_dir)\n"
|
|||
|
|
],
|
|||
|
|
"metadata": {
|
|||
|
|
"colab": {
|
|||
|
|
"base_uri": "https://localhost:8080/"
|
|||
|
|
},
|
|||
|
|
"id": "fJAqcU8F-p2Z",
|
|||
|
|
"outputId": "802ac1fd-b8de-4cec-b146-f3044a836c3f"
|
|||
|
|
},
|
|||
|
|
"execution_count": 40,
|
|||
|
|
"outputs": [
|
|||
|
|
{
|
|||
|
|
"output_type": "stream",
|
|||
|
|
"name": "stdout",
|
|||
|
|
"text": [
|
|||
|
|
"Mounted at /content/gdrive/\n",
|
|||
|
|
"Saving model to /content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/BERT_custom_model\n"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"output_type": "execute_result",
|
|||
|
|
"data": {
|
|||
|
|
"text/plain": [
|
|||
|
|
"('/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/BERT_custom_model/tokenizer_config.json',\n",
|
|||
|
|
" '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/BERT_custom_model/special_tokens_map.json',\n",
|
|||
|
|
" '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/BERT_custom_model/vocab.txt',\n",
|
|||
|
|
" '/content/gdrive/My Drive/UAM/Przetwarzanie-tekstu/BERT_custom_model/added_tokens.json')"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
"metadata": {},
|
|||
|
|
"execution_count": 40
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"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"
|
|||
|
|
}
|
|||
|
|
}
|
|||
|
|
]
|
|||
|
|
}
|