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7_neural_b
| Author | SHA1 | Date | |
|---|---|---|---|
| 39e6371df8 |
423
MOJ_7ipynb.ipynb
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423
MOJ_7ipynb.ipynb
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@ -0,0 +1,423 @@
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||||
{
|
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"nbformat": 4,
|
||||
"nbformat_minor": 0,
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"metadata": {
|
||||
"colab": {
|
||||
"provenance": [],
|
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"gpuType": "T4",
|
||||
"machine_shape": "hm"
|
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},
|
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"kernelspec": {
|
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"name": "python3",
|
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"display_name": "Python 3"
|
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},
|
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"language_info": {
|
||||
"name": "python"
|
||||
},
|
||||
"accelerator": "GPU"
|
||||
},
|
||||
"cells": [
|
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{
|
||||
"cell_type": "code",
|
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"execution_count": 4,
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"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
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},
|
||||
"id": "3dV_4SJ2xY_C",
|
||||
"outputId": "c1039907-474a-427a-ee13-62a3b7b4a693"
|
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},
|
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"outputs": [
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{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
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"text": [
|
||||
"Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
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]
|
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}
|
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],
|
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"source": [
|
||||
"from google.colab import drive\n",
|
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"drive.mount(\"/content/gdrive\")"
|
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]
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},
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{
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"cell_type": "code",
|
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"source": [
|
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"# %env DATA_DIR=/content/gdrive/MyDrive/data_gralinski\n",
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"DATA_DIR=\"/content/gdrive/MyDrive/data_gralinski/\""
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||||
],
|
||||
"metadata": {
|
||||
"id": "VwdW1Qm3x9-N"
|
||||
},
|
||||
"execution_count": 5,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
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||||
"source": [
|
||||
"import itertools\n",
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"import lzma\n",
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"import regex as re\n",
|
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"import torch\n",
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"from torch import nn\n",
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"from torch.utils.data import IterableDataset, DataLoader\n",
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"from torchtext.vocab import build_vocab_from_iterator"
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],
|
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"metadata": {
|
||||
"id": "irsty5KcyYkR"
|
||||
},
|
||||
"execution_count": 6,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"def clean_line(line: str):\n",
|
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" separated = line.split('\\t')\n",
|
||||
" prefix = separated[6].replace(r'\\n', ' ')\n",
|
||||
" suffix = separated[7].replace(r'\\n', ' ')\n",
|
||||
" return prefix + ' ' + suffix"
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],
|
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"metadata": {
|
||||
"id": "LXXtiKW3yY5J"
|
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},
|
||||
"execution_count": 7,
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||||
"outputs": []
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},
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{
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"cell_type": "code",
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"source": [
|
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"def get_words_from_line(line):\n",
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" line = clean_line(line)\n",
|
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" for word in line.split():\n",
|
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" yield word"
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],
|
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"metadata": {
|
||||
"id": "y9r0wmD3ycIi"
|
||||
},
|
||||
"execution_count": 8,
|
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"outputs": []
|
||||
},
|
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{
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"cell_type": "code",
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"source": [
|
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"def get_word_lines_from_file(file_name):\n",
|
||||
" with lzma.open(file_name, mode='rt', encoding='utf-8') as fid:\n",
|
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" for line in fid:\n",
|
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" yield get_words_from_line(line)"
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],
|
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"metadata": {
|
||||
"id": "HE3YfiHkycKt"
|
||||
},
|
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"execution_count": 9,
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"outputs": []
|
||||
},
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||||
{
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||||
"cell_type": "code",
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||||
"source": [
|
||||
"def look_ahead_iterator(gen):\n",
|
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" prev = None\n",
|
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" for item in gen:\n",
|
||||
" if prev is not None:\n",
|
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" yield (prev, item)\n",
|
||||
" prev = item"
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||||
],
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"metadata": {
|
||||
"id": "lvHvJV6XycNZ"
|
||||
},
|
||||
"execution_count": 10,
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||||
"outputs": []
|
||||
},
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{
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"cell_type": "code",
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"source": [
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"def prediction(word: str) -> str:\n",
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" ixs = torch.tensor(vocab.forward([word])).to(device)\n",
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" out = model(ixs)\n",
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" top = torch.topk(out[0], 5)\n",
|
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" top_indices = top.indices.tolist()\n",
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" top_probs = top.values.tolist()\n",
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" top_words = vocab.lookup_tokens(top_indices)\n",
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" zipped = list(zip(top_words, top_probs))\n",
|
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" for index, element in enumerate(zipped):\n",
|
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" unk = None\n",
|
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" if '<unk>' in element:\n",
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" unk = zipped.pop(index)\n",
|
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" zipped.append(('', unk[1]))\n",
|
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" break\n",
|
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" if unk is None:\n",
|
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" zipped[-1] = ('', zipped[-1][1])\n",
|
||||
" return ' '.join([f'{x[0]}:{x[1]}' for x in zipped])"
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],
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"metadata": {
|
||||
"id": "sOKeZN9cycP-"
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},
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"execution_count": 11,
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"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
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"source": [
|
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"def create_outputs(folder_name):\n",
|
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" print(f'Creating outputs in {folder_name}')\n",
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" with lzma.open(f'{folder_name}/in.tsv.xz', mode='rt', encoding='utf-8') as fid:\n",
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||||
" with open(f'{folder_name}/out2.tsv', 'w', encoding='utf-8', newline='\\n') as f:\n",
|
||||
" for line in fid:\n",
|
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" separated = line.split('\\t')\n",
|
||||
" prefix = separated[6].replace(r'\\n', ' ').split()[-1]\n",
|
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" output_line = prediction(prefix)\n",
|
||||
" f.write(output_line + '\\n')"
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],
|
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"metadata": {
|
||||
"id": "MN_RftZNycSB"
|
||||
},
|
||||
"execution_count": 30,
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||||
"outputs": []
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||||
},
|
||||
{
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||||
"cell_type": "code",
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"source": [
|
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"class Bigrams(IterableDataset):\n",
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" def __init__(self, text_file, vocabulary_size):\n",
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" self.vocab = build_vocab_from_iterator(\n",
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" get_word_lines_from_file(text_file),\n",
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" max_tokens=vocabulary_size,\n",
|
||||
" specials=['<unk>'])\n",
|
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" self.vocab.set_default_index(self.vocab['<unk>'])\n",
|
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" self.vocabulary_size = vocabulary_size\n",
|
||||
" self.text_file = text_file\n",
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"\n",
|
||||
" def __iter__(self):\n",
|
||||
" return look_ahead_iterator(\n",
|
||||
" (self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file))))"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "n9wIsbLEycUd"
|
||||
},
|
||||
"execution_count": 13,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"class SimpleBigramNeuralLanguageModel(nn.Module):\n",
|
||||
" def __init__(self, vocabulary_size, embedding_size):\n",
|
||||
" super(SimpleBigramNeuralLanguageModel, self).__init__()\n",
|
||||
" self.model = nn.Sequential(\n",
|
||||
" nn.Embedding(vocabulary_size, embedding_size),\n",
|
||||
" nn.Linear(embedding_size, vocabulary_size),\n",
|
||||
" nn.Softmax()\n",
|
||||
" )\n",
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||||
"\n",
|
||||
" def forward(self, x):\n",
|
||||
" return self.model(x)"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "l490B5KFycXj"
|
||||
},
|
||||
"execution_count": 14,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"vocab_size = 15000\n",
|
||||
"embed_size = 300\n",
|
||||
"batch_size = 3000\n",
|
||||
"device = 'cuda'\n",
|
||||
"path_to_train = DATA_DIR+'train/in.tsv.xz'\n",
|
||||
"path_to_model = 'model.bin'"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "mMC84-OzycZ5"
|
||||
},
|
||||
"execution_count": 21,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"vocab = build_vocab_from_iterator(\n",
|
||||
" get_word_lines_from_file(path_to_train),\n",
|
||||
" max_tokens=vocab_size,\n",
|
||||
" specials=['<unk>']\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"vocab.set_default_index(vocab['<unk>'])"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "Fsvv3QJl7kWN"
|
||||
},
|
||||
"execution_count": 22,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"train_dataset = Bigrams(path_to_train, vocab_size)"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "UK73WsKnB8ZP"
|
||||
},
|
||||
"execution_count": 23,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
|
||||
"data = DataLoader(train_dataset, batch_size=batch_size)\n",
|
||||
"optimizer = torch.optim.Adam(model.parameters())\n",
|
||||
"criterion = torch.nn.NLLLoss()\n",
|
||||
"\n",
|
||||
"model.train()\n",
|
||||
"step = 0\n",
|
||||
"for x, y in data:\n",
|
||||
" x = x.to(device)\n",
|
||||
" y = y.to(device)\n",
|
||||
" optimizer.zero_grad()\n",
|
||||
" ypredicted = model(x)\n",
|
||||
" loss = criterion(torch.log(ypredicted), y)\n",
|
||||
" if step % 5000 == 0:\n",
|
||||
" print(step, loss)\n",
|
||||
" step += 1\n",
|
||||
" loss.backward()\n",
|
||||
" optimizer.step()"
|
||||
],
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "Q_Gz-stnqHPg",
|
||||
"outputId": "4b6a3751-21da-48a9-afcb-802b21f7274b"
|
||||
},
|
||||
"execution_count": 24,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stderr",
|
||||
"text": [
|
||||
"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/container.py:217: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
|
||||
" input = module(input)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": [
|
||||
"0 tensor(9.8217, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"5000 tensor(5.3191, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"10000 tensor(5.0986, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"15000 tensor(5.2346, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"20000 tensor(5.4174, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"25000 tensor(5.1875, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"30000 tensor(5.1892, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"35000 tensor(5.0867, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"40000 tensor(5.1812, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
|
||||
"45000 tensor(5.1327, device='cuda:0', grad_fn=<NllLossBackward0>)\n"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"torch.save(model.state_dict(), path_to_model)"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "WTl82y44qHR_"
|
||||
},
|
||||
"execution_count": 25,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
|
||||
"model.load_state_dict(torch.load(path_to_model))\n",
|
||||
"model.eval()"
|
||||
],
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "jViNAbxIqHUr",
|
||||
"outputId": "3c30bed7-8eaf-4e7b-eb89-89bdcf95abe7"
|
||||
},
|
||||
"execution_count": 26,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"SimpleBigramNeuralLanguageModel(\n",
|
||||
" (model): Sequential(\n",
|
||||
" (0): Embedding(15000, 300)\n",
|
||||
" (1): Linear(in_features=300, out_features=15000, bias=True)\n",
|
||||
" (2): Softmax(dim=None)\n",
|
||||
" )\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 26
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"create_outputs(DATA_DIR+'dev-0')"
|
||||
],
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "LkHFiPvoqHW8",
|
||||
"outputId": "d4d12906-5f4c-4df6-a22e-95a4fbbd9850"
|
||||
},
|
||||
"execution_count": 29,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": [
|
||||
"Creating outputs in /content/gdrive/MyDrive/data_gralinski/dev-0\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stderr",
|
||||
"text": [
|
||||
"/usr/local/lib/python3.10/dist-packages/torch/nn/modules/container.py:217: UserWarning: Implicit dimension choice for softmax has been deprecated. Change the call to include dim=X as an argument.\n",
|
||||
" input = module(input)\n"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"create_outputs(DATA_DIR+'test-A')"
|
||||
],
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "QoiHDZ_ZqO8F",
|
||||
"outputId": "0fc59359-ce83-4a43-d11b-8d43280df3a1"
|
||||
},
|
||||
"execution_count": 31,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": [
|
||||
"Creating outputs in /content/gdrive/MyDrive/data_gralinski/test-A\n"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
10519
dev-0/out.tsv
Normal file
10519
dev-0/out.tsv
Normal file
File diff suppressed because it is too large
Load Diff
18
gonito.yaml
18
gonito.yaml
@ -1,10 +1,16 @@
|
||||
description: trigram model
|
||||
description: neurl-network bigram
|
||||
tags:
|
||||
- neural-network
|
||||
- trigram
|
||||
- bigram
|
||||
params:
|
||||
epochs: 1
|
||||
learning-rate: 0.0001
|
||||
vocab_size: 40000
|
||||
embed_size: 300
|
||||
hidden_size: 256
|
||||
learning-rate: 0.001
|
||||
unwanted-params:
|
||||
- model-file
|
||||
- vocab-file
|
||||
param-files:
|
||||
- "*.yaml"
|
||||
- config/*.yaml
|
||||
links:
|
||||
- title: "repository"
|
||||
url: "https://git.wmi.amu.edu.pl/s444354/challenging-america-word-gap-prediction.git"
|
||||
|
||||
7414
test-A/out.tsv
Normal file
7414
test-A/out.tsv
Normal file
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue
Block a user