neural bigrams
This commit is contained in:
parent
37a762f54e
commit
c6c596193a
|
|
@ -0,0 +1,409 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"## Imports"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"pycharm": {
|
||||
"is_executing": true
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import itertools\n",
|
||||
"import lzma\n",
|
||||
"\n",
|
||||
"import regex as re\n",
|
||||
"import torch\n",
|
||||
"from torch import nn\n",
|
||||
"from torch.utils.data import IterableDataset, DataLoader\n",
|
||||
"from torchtext.vocab import build_vocab_from_iterator"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from google.colab import drive"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"## Definitions"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"### Functions"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def clean_line(line: str):\n",
|
||||
" # Preprocessing\n",
|
||||
" separated = line.split('\\t')\n",
|
||||
" prefix = separated[6].replace(r'\\n', ' ')\n",
|
||||
" suffix = separated[7].replace(r'\\n', ' ')\n",
|
||||
" return prefix + ' ' + suffix"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def get_words_from_line(line):\n",
|
||||
" line = clean_line(line)\n",
|
||||
" for m in re.finditer(r'[\\p{L}0-9\\*]+|\\p{P}+', line):\n",
|
||||
" yield m.group(0).lower()"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def get_word_lines_from_file(file_name):\n",
|
||||
" with lzma.open(file_name, mode='rt', encoding='utf-8') as fid:\n",
|
||||
" for line in fid:\n",
|
||||
" yield get_words_from_line(line)"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def look_ahead_iterator(gen):\n",
|
||||
" prev = None\n",
|
||||
" for item in gen:\n",
|
||||
" if prev is not None:\n",
|
||||
" yield (prev, item)\n",
|
||||
" prev = item"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def prediction(word: str) -> str:\n",
|
||||
" ixs = torch.tensor(vocab.forward([word])).to(device)\n",
|
||||
" out = model(ixs)\n",
|
||||
" top = torch.topk(out[0], 5)\n",
|
||||
" top_indices = top.indices.tolist()\n",
|
||||
" top_probs = top.values.tolist()\n",
|
||||
" top_words = vocab.lookup_tokens(top_indices)\n",
|
||||
" zipped = list(zip(top_words, top_probs))\n",
|
||||
" for index, element in enumerate(zipped):\n",
|
||||
" unk = None\n",
|
||||
" if '<unk>' in element:\n",
|
||||
" unk = zipped.pop(index)\n",
|
||||
" zipped.append(('', unk[1]))\n",
|
||||
" break\n",
|
||||
" if unk is None:\n",
|
||||
" zipped[-1] = ('', zipped[-1][1])\n",
|
||||
" return ' '.join([f'{x[0]}:{x[1]}' for x in zipped])"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def create_outputs(folder_name):\n",
|
||||
" print(f'Creating outputs in {folder_name}')\n",
|
||||
" with lzma.open(f'{folder_name}/in.tsv.xz', mode='rt', encoding='utf-8') as fid:\n",
|
||||
" with open(f'{folder_name}/out.tsv', 'w', encoding='utf-8', newline='\\n') as f:\n",
|
||||
" for line in fid:\n",
|
||||
" separated = line.split('\\t')\n",
|
||||
" prefix = separated[6].replace(r'\\n', ' ').split()[-1]\n",
|
||||
" output_line = prediction(prefix)\n",
|
||||
" f.write(output_line + '\\n')"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"### Classes"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class Bigrams(IterableDataset):\n",
|
||||
" def __init__(self, text_file, vocabulary_size):\n",
|
||||
" self.vocab = build_vocab_from_iterator(\n",
|
||||
" get_word_lines_from_file(text_file),\n",
|
||||
" max_tokens=vocabulary_size,\n",
|
||||
" specials=['<unk>'])\n",
|
||||
" self.vocab.set_default_index(self.vocab['<unk>'])\n",
|
||||
" self.vocabulary_size = vocabulary_size\n",
|
||||
" self.text_file = text_file\n",
|
||||
"\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": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"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",
|
||||
"\n",
|
||||
" def forward(self, x):\n",
|
||||
" return self.model(x)"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"## Training"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"### Params"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"vocab_size = 15000\n",
|
||||
"embed_size = 150\n",
|
||||
"batch_size = 3000\n",
|
||||
"device = 'cuda'\n",
|
||||
"path_to_train = 'train/in.tsv.xz'\n",
|
||||
"path_to_model = 'model1.bin'"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"### Colab"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"drive.mount('/content/drive')\n",
|
||||
"%cd /content/drive/MyDrive/"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"### Run"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"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": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"train_dataset = Bigrams(path_to_train, vocab_size)"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"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 % 100 == 0:\n",
|
||||
" print(step, loss)\n",
|
||||
" step += 1\n",
|
||||
" loss.backward()\n",
|
||||
" optimizer.step()"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"torch.save(model.state_dict(), path_to_model)"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
|
||||
"model.load_state_dict(torch.load(path_to_model))\n",
|
||||
"model.eval()"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"create_outputs('dev-0')"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"create_outputs('test-A')"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 2
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython2",
|
||||
"version": "2.7.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
21038
dev-0/out.tsv
21038
dev-0/out.tsv
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,16 @@
|
|||
description: nn bigram solution
|
||||
tags:
|
||||
- neural-network
|
||||
- bigram
|
||||
params:
|
||||
epochs: 1
|
||||
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/s444501/challenging-america-word-gap-prediction.git"
|
||||
14828
test-A/out.tsv
14828
test-A/out.tsv
File diff suppressed because it is too large
Load Diff
Loading…
Reference in New Issue