add solution code
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Kacper
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commit
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solution.ipynb
561
solution.ipynb
@ -1,329 +1,236 @@
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"provenance": []
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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": {
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"name": "python"
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},
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"accelerator": "GPU",
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"gpuClass": "standard"
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},
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "5oSsy7tRYrXO",
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"outputId": "896cbe7d-61a5-44b0-b4fb-ba308c6ea7b2"
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},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Cloning into 'challenging-america-word-gap-prediction'...\n",
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"remote: Wymienianie obiektów: 27, gotowe.\u001b[K\n",
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"remote: Zliczanie obiektów: 100% (27/27), gotowe.\u001b[K\n",
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"remote: Kompresowanie obiektów: 100% (23/23), gotowe.\u001b[K\n",
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"remote: Razem 27 (delty 2), użyte ponownie 18 (delty 0), paczki użyte ponownie 0\u001b[K\n",
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"Receiving objects: 100% (27/27), 278.33 MiB | 8.66 MiB/s, done.\n",
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"Resolving deltas: 100% (2/2), done.\n"
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]
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}
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],
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"source": [
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" !git clone --single-branch git://gonito.net/challenging-america-word-gap-prediction -b master"
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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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"from torchtext.vocab import build_vocab_from_iterator\n",
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"import pickle\n",
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"from torch.utils.data import IterableDataset\n",
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"import itertools\n",
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"from torch import nn\n",
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"import torch\n",
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"import lzma\n",
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"from torch.utils.data import DataLoader\n",
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"from tqdm import tqdm"
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],
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"metadata": {
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"id": "WnglOFA8gGJl"
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},
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"execution_count": 6,
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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 simple_preprocess(line):\n",
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" return line.replace(r'\\n', ' ')\n",
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"\n",
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"def get_words_from_line(line):\n",
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" line = line.strip()\n",
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" line = simple_preprocess(line)\n",
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" yield '<s>'\n",
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" for t in line.split():\n",
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" yield t\n",
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" yield '</s>'\n",
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"\n",
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"def get_word_lines_from_file(file_name, n_size=-1):\n",
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" with lzma.open(file_name, 'r') as fh:\n",
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" n = 0\n",
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" for line in fh:\n",
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" n += 1\n",
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" yield get_words_from_line(line.decode('utf-8'))\n",
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" if n == n_size:\n",
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" break\n",
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"\n",
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"def look_ahead_iterator(gen):\n",
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" prev = None\n",
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" for item in gen:\n",
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" if prev is not None:\n",
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" yield prev, item\n",
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" prev = item\n",
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"\n",
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"def build_vocab(file, vocab_size):\n",
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" try:\n",
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" with open(f'vocab_{vocab_size}.pickle', 'rb') as handle:\n",
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" vocab = pickle.load(handle)\n",
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" except:\n",
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" vocab = build_vocab_from_iterator(\n",
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" get_word_lines_from_file(file),\n",
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" max_tokens = vocab_size,\n",
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" specials = ['<unk>'])\n",
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" with open(f'vocab_{vocab_size}.pickle', 'wb') as handle:\n",
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" pickle.dump(vocab, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
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" return vocab\n",
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"\n",
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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 = vocab\n",
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" self.vocab.set_default_index(self.vocab['<unk>'])\n",
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" self.vocabulary_size = vocabulary_size\n",
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" self.text_file = text_file\n",
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"\n",
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" def __iter__(self):\n",
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" return look_ahead_iterator(\n",
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" (self.vocab[t] for t in itertools.chain.from_iterable(get_word_lines_from_file(self.text_file))))\n",
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"\n",
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"class SimpleBigramNeuralLanguageModel(nn.Module):\n",
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" def __init__(self, vocabulary_size, embedding_size):\n",
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" super(SimpleBigramNeuralLanguageModel, self).__init__()\n",
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" self.model = nn.Sequential(\n",
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" nn.Embedding(vocabulary_size, embedding_size),\n",
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" nn.Linear(embedding_size, vocabulary_size),\n",
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" nn.Softmax()\n",
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" )\n",
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"\n",
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" def forward(self, x):\n",
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" return self.model(x)"
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],
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"metadata": {
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"id": "aW_3JqSNgLLr"
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},
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"execution_count": 25,
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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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"max_steps=-1\n",
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"vocab_size = 5000\n",
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"embed_size = 50\n",
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"batch_size = 5000\n",
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"vocab = build_vocab('challenging-america-word-gap-prediction/train/in.tsv.xz', vocab_size)\n",
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"train_dataset = Bigrams('challenging-america-word-gap-prediction/train/in.tsv.xz', vocab_size)\n",
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"if torch.cuda.is_available():\n",
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" device = 'cuda'\n",
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"else:\n",
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" raise Exception()\n",
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"model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
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"data = DataLoader(train_dataset, batch_size=batch_size)\n",
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"optimizer = torch.optim.Adam(model.parameters())\n",
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"criterion = torch.nn.NLLLoss()\n",
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"\n",
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"model.train()\n",
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"step = 0\n",
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"for x, y in data:\n",
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" x = x.to(device)\n",
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" y = y.to(device)\n",
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" optimizer.zero_grad()\n",
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" ypredicted = model(x)\n",
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" loss = criterion(torch.log(ypredicted), y)\n",
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" if step % 1000 == 0:\n",
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" print(step, loss)\n",
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" if step % 1000 == 0:\n",
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" torch.save(model.state_dict(), f'model_steps-{step}_vocab-{vocab_size}_embed-{embed_size}_batch-{batch_size}.bin')\n",
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" if step == max_steps:\n",
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" break\n",
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" step += 1\n",
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" loss.backward()\n",
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" optimizer.step()"
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "QQw_E7Ku4h0a",
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"outputId": "4a37d9ba-1abd-46ae-b157-cd6d52b951a2"
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},
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"execution_count": 11,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Currently training: model_steps--1_vocab-5000_embed-50_batch-5000.bin\n"
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]
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},
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{
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"output_type": "stream",
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"name": "stderr",
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"text": [
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"/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",
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" input = module(input)\n"
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]
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},
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"0 tensor(8.6451, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"1000 tensor(4.7971, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"2000 tensor(4.7606, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"3000 tensor(4.5784, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"4000 tensor(4.5029, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"5000 tensor(4.6751, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"6000 tensor(4.4452, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"7000 tensor(4.4145, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"8000 tensor(4.5194, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"9000 tensor(4.4242, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"10000 tensor(4.2885, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"11000 tensor(4.3033, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"12000 tensor(4.4238, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"13000 tensor(4.5368, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"14000 tensor(4.3551, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"15000 tensor(4.3116, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"16000 tensor(4.3750, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"17000 tensor(4.4356, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"18000 tensor(4.4206, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"19000 tensor(4.5120, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"20000 tensor(4.4687, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"21000 tensor(4.3365, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"22000 tensor(4.3464, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"23000 tensor(4.4861, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"24000 tensor(4.3531, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"25000 tensor(4.3431, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"26000 tensor(4.3747, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"27000 tensor(4.2183, device='cuda:0', grad_fn=<NllLossBackward0>)\n",
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"28000 tensor(4.4097, device='cuda:0', grad_fn=<NllLossBackward0>)\n"
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]
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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": [
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"# vocab_size = 5000\n",
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"# embed_size = 50\n",
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"# batch_size = 5000\n",
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"# vocab = build_vocab('challenging-america-word-gap-prediction/train/in.tsv.xz', vocab_size)\n",
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"# vocab.set_default_index(vocab['<unk>'])\n",
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"\n",
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"vocab_size = 20000\n",
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"embed_size = 100\n",
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"batch_size = 5000\n",
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"vocab = build_vocab('challenging-america-word-gap-prediction/train/in.tsv.xz', vocab_size)\n",
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"vocab.set_default_index(vocab['<unk>'])"
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],
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"metadata": {
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"id": "N9-wmLOEZ2aV"
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},
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"execution_count": 42,
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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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"preds = []\n",
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"device = 'cuda'\n",
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"model = SimpleBigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
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"model.load_state_dict(torch.load('/content/model_steps-27000_vocab-5000_embed-50_batch-5000.bin'))\n",
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"model.eval()\n",
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"j = 0\n",
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"for path in ['challenging-america-word-gap-prediction/dev-0', 'challenging-america-word-gap-prediction/test-A']:\n",
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" with lzma.open(f'{path}/in.tsv.xz', 'r') as fh, open(f'{path}/out.tsv', 'w', encoding='utf-8') as f_out:\n",
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" for line in fh:\n",
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" previous_word = simple_preprocess(line.decode('utf-8').split('\\t')[-2]).split()[-1]\n",
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" ixs = torch.tensor(vocab.forward([previous_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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" top_zipped = list(zip(top_words, top_probs))\n",
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" pred = ''\n",
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" unk = None\n",
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" for i, tup in enumerate(top_zipped):\n",
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" if tup[0] == '<unk>':\n",
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" unk = top_zipped.pop(i)\n",
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" for tup in top_zipped:\n",
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" pred += f'{tup[0]}:{tup[1]}\\t'\n",
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" if unk:\n",
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" pred += f':{unk[1]}'\n",
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" else:\n",
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" pred = pred.rstrip()\n",
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" f_out.write(pred + '\\n')\n",
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" if j % 1000 == 0:\n",
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" print(pred)\n",
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" j += 1 "
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],
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "99uioFpVCJL8",
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"outputId": "d4267cb1-e557-478a-8cf7-91a90db07698"
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},
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"execution_count": 48,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"the:0.32605835795402527\ta:0.03863263502717018\this:0.019891299307346344\ttho:0.017584890127182007\t:0.1336958259344101\n",
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"same:0.008983609266579151\tmost:0.006951075047254562\tfirst:0.005848093423992395\tUnited:0.005354634020477533\t:0.22962644696235657\n",
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"of:0.1870267689228058\tNo.:0.05885934457182884\tand:0.0347345806658268\tnumbered:0.017088865861296654\t:0.12375127524137497\n",
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"the:0.23099401593208313\ta:0.05134483054280281\this:0.017109891399741173\tthis:0.015690239146351814\t:0.2021108716726303\n",
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||||
"is:0.16247524321079254\twas:0.08097667992115021\twill:0.03666245937347412\twould:0.031893592327833176\t:0.09085553884506226\n",
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"the:0.14925561845302582\tbe:0.07023955136537552\ta:0.0237724632024765\thave:0.0131039097905159\t:0.12894178926944733\n",
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"years:0.11707684397697449\tmiles:0.038641661405563354\tacres:0.0361776202917099\tdays:0.035523977130651474\t:0.1676659733057022\n",
|
||||
"and:0.05091285705566406\tof:0.03853045403957367\tthe:0.02558819204568863\tto:0.019778745248913765\t:0.2338942289352417\n",
|
||||
"to:0.20445719361305237\tthe:0.13792230188846588\ta:0.04136090725660324\tby:0.02959897182881832\t:0.06412851065397263\n",
|
||||
"the:0.14456485211849213\the:0.0543459951877594\tthey:0.0345623604953289\tit:0.03187565878033638\t:0.08283700793981552\n",
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||||
"to:0.11275122314691544\tof:0.07946161180734634\tlike:0.056227609515190125\tthat:0.05296172574162483\t:0.1051449254155159\n",
|
||||
"of:0.04079027101397514\tday:0.0400676503777504\ttime:0.02808181196451187\tto:0.02239527367055416\t:0.147441565990448\n",
|
||||
"on:0.28541672229766846\tat:0.043499380350112915\tthe:0.04269522428512573\tin:0.03935478255152702\t:0.10247787833213806\n",
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".:0.26101377606391907\t.,:0.046980664134025574\tand:0.009626681916415691\tM:0.007779326289892197\t:0.3348052203655243\n",
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"and:0.05091285705566406\tof:0.03853045403957367\tthe:0.02558819204568863\tto:0.019778745248913765\t:0.2338942289352417\n",
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"the:0.4567626714706421\tsaid:0.053911514580249786\twith:0.04098761826753616\tand:0.02215263620018959\t:0.07401206344366074\n",
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"and:0.19774483144283295\tbut:0.03353063389658928\tthe:0.029393238946795464\tas:0.026280701160430908\t:0.06644411385059357\n",
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"and:0.15652838349342346\twho:0.038931723684072495\tbut:0.036329541355371475\tthe:0.03554282337427139\t:0.05828680843114853\n"
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||||
]
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||||
}
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]
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||||
"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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||||
"collapsed": true,
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||||
"pycharm": {
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||||
"is_executing": true
|
||||
}
|
||||
]
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from torchtext.vocab import build_vocab_from_iterator\n",
|
||||
"import pickle\n",
|
||||
"from torch.utils.data import IterableDataset\n",
|
||||
"from itertools import chain\n",
|
||||
"from torch import nn\n",
|
||||
"import torch.nn.functional as F\n",
|
||||
"import torch\n",
|
||||
"import lzma\n",
|
||||
"from torch.utils.data import DataLoader\n",
|
||||
"import shutil\n",
|
||||
"torch.manual_seed(1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 65,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def simple_preprocess(line):\n",
|
||||
" return line.replace(r'\\n', ' ')\n",
|
||||
"\n",
|
||||
"def get_words_from_line(line):\n",
|
||||
" line = line.strip()\n",
|
||||
" line = simple_preprocess(line)\n",
|
||||
" yield '<s>'\n",
|
||||
" for t in line.split():\n",
|
||||
" yield t\n",
|
||||
" yield '</s>'\n",
|
||||
"\n",
|
||||
"def get_word_lines_from_file(file_name, n_size=-1):\n",
|
||||
" with lzma.open(file_name, 'r') as fh:\n",
|
||||
" n = 0\n",
|
||||
" for line in fh:\n",
|
||||
" n += 1\n",
|
||||
" yield get_words_from_line(line.decode('utf-8'))\n",
|
||||
" if n == n_size:\n",
|
||||
" break\n",
|
||||
"\n",
|
||||
"def look_ahead_iterator(gen):\n",
|
||||
" ngram = []\n",
|
||||
" for item in gen:\n",
|
||||
" if len(ngram) < 3:\n",
|
||||
" ngram.append(item)\n",
|
||||
" if len(ngram) == 3:\n",
|
||||
" yield ngram[1], ngram[2], ngram[0]\n",
|
||||
" else:\n",
|
||||
" ngram = ngram[1:]\n",
|
||||
" ngram.append(item)\n",
|
||||
" yield ngram[1], ngram[2], ngram[0]\n",
|
||||
"\n",
|
||||
"def build_vocab(file, vocab_size):\n",
|
||||
" try:\n",
|
||||
" with open(f'vocab_{vocab_size}.pickle', 'rb') as handle:\n",
|
||||
" vocab = pickle.load(handle)\n",
|
||||
" except:\n",
|
||||
" vocab = build_vocab_from_iterator(\n",
|
||||
" get_word_lines_from_file(file),\n",
|
||||
" max_tokens = vocab_size,\n",
|
||||
" specials = ['<unk>'])\n",
|
||||
" with open(f'vocab_{vocab_size}.pickle', 'wb') as handle:\n",
|
||||
" pickle.dump(vocab, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
|
||||
" return vocab\n",
|
||||
"\n",
|
||||
"class Trigrams(IterableDataset):\n",
|
||||
" def __init__(self, text_file):\n",
|
||||
" self.vocab = vocab\n",
|
||||
" self.vocab.set_default_index(self.vocab['<unk>'])\n",
|
||||
" self.text_file = text_file\n",
|
||||
"\n",
|
||||
" def __iter__(self):\n",
|
||||
" return look_ahead_iterator(\n",
|
||||
" (self.vocab[t] for t in chain.from_iterable(get_word_lines_from_file(self.text_file))))\n",
|
||||
"\n",
|
||||
"class TrigramNeuralLanguageModel(nn.Module):\n",
|
||||
" def __init__(self, vocab_size, embed_size):\n",
|
||||
" super(TrigramNeuralLanguageModel, self).__init__()\n",
|
||||
" self.embeddings = nn.Embedding(vocab_size, embed_size)\n",
|
||||
" self.hidden_layer = nn.Linear(2*embed_size, 64)\n",
|
||||
" self.output_layer = nn.Linear(64, vocab_size)\n",
|
||||
"\n",
|
||||
" def forward(self, x):\n",
|
||||
" embeds = self.embeddings(x[0]), self.embeddings(x[1])\n",
|
||||
" concat_embed = torch.concat(embeds, dim=1)\n",
|
||||
" z = F.relu(self.hidden_layer(concat_embed))\n",
|
||||
" softmax = nn.Softmax(dim=1)\n",
|
||||
" y = softmax(self.output_layer(z))\n",
|
||||
" return y"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"max_steps = -1\n",
|
||||
"vocab_size = 5000\n",
|
||||
"embed_size = 50\n",
|
||||
"batch_size = 5000\n",
|
||||
"vocab = build_vocab('challenging-america-word-gap-prediction/train/in.tsv.xz', vocab_size)\n",
|
||||
"train_dataset = Trigrams('challenging-america-word-gap-prediction/train/in.tsv.xz')\n",
|
||||
"if torch.cuda.is_available():\n",
|
||||
" device = 'cuda'\n",
|
||||
"else:\n",
|
||||
" raise Exception()\n",
|
||||
"model = TrigramNeuralLanguageModel(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 x1, x2, y in data:\n",
|
||||
" x = x1.to(device), x2.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 % 1000 == 0:\n",
|
||||
" print(step, loss)\n",
|
||||
" if step % 1000 == 0:\n",
|
||||
" torch.save(model.state_dict(), f'model_steps-{step}_vocab-{vocab_size}_embed-{embed_size}_batch-{batch_size}.bin')\n",
|
||||
" loss.backward()\n",
|
||||
" optimizer.step()\n",
|
||||
" if step == max_steps:\n",
|
||||
" break\n",
|
||||
" step += 1"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"is_executing": true
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"vocab_size = 5000\n",
|
||||
"embed_size = 50\n",
|
||||
"batch_size = 5000\n",
|
||||
"vocab = build_vocab('challenging-america-word-gap-prediction/train/in.tsv.xz', vocab_size)\n",
|
||||
"vocab.set_default_index(vocab['<unk>'])"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"for model_name in ['model_steps-1000_vocab-5000_embed-50_batch-5000.bin',\n",
|
||||
" 'model_steps-1000_vocab-5000_embed-50_batch-5000.bin', 'model_steps-27000_vocab-5000_embed-50_batch-5000.bin']:\n",
|
||||
" preds = []\n",
|
||||
" device = 'cuda'\n",
|
||||
" model = TrigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
|
||||
" model.load_state_dict(torch.load(model_name))\n",
|
||||
" model.eval()\n",
|
||||
" j = 0\n",
|
||||
" for path in ['challenging-america-word-gap-prediction/dev-0', 'challenging-america-word-gap-prediction/test-A']:\n",
|
||||
" with lzma.open(f'{path}/in.tsv.xz', 'r') as fh, open(f'{path}/out.tsv', 'w', encoding='utf-8') as f_out:\n",
|
||||
" for line in fh:\n",
|
||||
" right_context = simple_preprocess(line.decode('utf-8').split('\\t')[-1]).split()[:2]\n",
|
||||
" x = torch.tensor(vocab.forward([right_context[0]])).to(device), \\\n",
|
||||
" torch.tensor(vocab.forward([right_context[1]])).to(device)\n",
|
||||
" out = model(x)\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",
|
||||
" top_zipped = list(zip(top_words, top_probs))\n",
|
||||
" pred = ''\n",
|
||||
" unk = None\n",
|
||||
" for i, tup in enumerate(top_zipped):\n",
|
||||
" if tup[0] == '<unk>':\n",
|
||||
" unk = top_zipped.pop(i)\n",
|
||||
" for tup in top_zipped:\n",
|
||||
" pred += f'{tup[0]}:{tup[1]}\\t'\n",
|
||||
" if unk:\n",
|
||||
" pred += f':{unk[1]}'\n",
|
||||
" else:\n",
|
||||
" pred = pred.rstrip()\n",
|
||||
" f_out.write(pred + '\\n')\n",
|
||||
" if j % 1000 == 0:\n",
|
||||
" print(pred)\n",
|
||||
" j += 1\n",
|
||||
" src=f'{path}/out.tsv'\n",
|
||||
" dst=f\"{path}/{model_name.split('.')[0]}_out.tsv\"\n",
|
||||
" shutil.copy(src, dst)"
|
||||
],
|
||||
"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
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue
Block a user