File size: 1,757 Bytes
28dc58b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 |
{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from bert_dataset import BERTDataset\n",
"from torch.utils.data import DataLoader\n",
"from bert_model import BERT, BERTLM\n",
"from trainer import BERTTrainer\n",
"from transformers import BertTokenizer\n",
"from data import get_data\n",
"\n",
"MAX_LEN = 128\n",
"\n",
"pairs = get_data('datasets/movie_conversations.txt', \"datasets/movie_lines.txt\")\n",
"tokenizer = BertTokenizer.from_pretrained(\"bert-it-1/bert-it-vocab.txt\")\n",
"\n",
"train_data = BERTDataset()\n",
"\n",
"train_loader = DataLoader(\n",
" train_data, batch_size=32, shuffle=True, pin_memory=True)\n",
"\n",
"bert_model = BERT(\n",
" vocab_size=len(tokenizer.vocab),\n",
" d_model=768,\n",
" n_layers=2,\n",
" heads=12,\n",
" dropout=0.1\n",
")\n",
"\n",
"bert_lm = BERTLM(bert=bert_model, vocab_size=len(tokenizer.vocab))\n",
"bert_trainer = BERTTrainer(bert_lm, train_loader, device='cpu')\n",
"epochs = 20\n",
"\n",
"for epoch in range(epochs):\n",
" bert_trainer.train(epoch)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|