fix inference and results
This commit is contained in:
Kacper
parent
dbda50ac2b
commit
c2fa4e59db
@ -1 +1,2 @@
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# Rozwiązanie dla wariantu kontekstu dwóch następnych słów (reszta z dzielenia przez 3 = 2)
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# Rozwiązanie dla wariantu kontekstu dwóch następnych słów (reszta z dzielenia przez 3 = 2)
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# Bugfixed inference and uploaded correctly generated results on 24.05.23.
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21038
dev-0/out.tsv
21038
dev-0/out.tsv
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@ -4,8 +4,10 @@ tags:
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- right-context
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- trigrams
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params:
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vocab_size: 5000
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embed_size: 50
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batch_size: 5000
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vocab_size: 20000
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embed_size: 150
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batch_size: 512, 1024, 4096
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hidden_size: 256, 1024
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learning_rate: 0.0001, 0.001
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param-files:
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- "*.yaml"
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165
solution.ipynb
165
solution.ipynb
@ -26,19 +26,19 @@
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},
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{
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"cell_type": "code",
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"execution_count": 65,
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"execution_count": 3,
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"outputs": [],
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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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" 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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" 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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@ -63,14 +63,14 @@
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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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" with open(f'trigram_nn_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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" with open(f'trigram_nn_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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@ -85,18 +85,18 @@
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" (self.vocab[t] for t in chain.from_iterable(get_word_lines_from_file(self.text_file))))\n",
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"\n",
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"class TrigramNeuralLanguageModel(nn.Module):\n",
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" def __init__(self, vocab_size, embed_size):\n",
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" def __init__(self, vocab_size, embed_size, hidden_size):\n",
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" super(TrigramNeuralLanguageModel, self).__init__()\n",
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" self.embeddings = nn.Embedding(vocab_size, embed_size)\n",
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" self.hidden_layer = nn.Linear(2*embed_size, 64)\n",
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" self.output_layer = nn.Linear(64, vocab_size)\n",
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" self.hidden_layer = nn.Linear(2*embed_size, hidden_size)\n",
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" self.output_layer = nn.Linear(hidden_size, vocab_size)\n",
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" self.softmax = nn.Softmax(dim=1)\n",
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"\n",
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" def forward(self, x):\n",
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" embeds = self.embeddings(x[0]), self.embeddings(x[1])\n",
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" concat_embed = torch.concat(embeds, dim=1)\n",
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" z = F.relu(self.hidden_layer(concat_embed))\n",
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" softmax = nn.Softmax(dim=1)\n",
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" y = softmax(self.output_layer(z))\n",
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" y = self.softmax(self.output_layer(z))\n",
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" return y"
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],
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"metadata": {
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@ -109,18 +109,20 @@
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"outputs": [],
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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_size = 20000\n",
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"embed_size = 150\n",
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"batch_size = 1024\n",
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"hidden_size = 1024\n",
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"learning_rate = 0.001\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 = Trigrams('challenging-america-word-gap-prediction/train/in.tsv.xz')\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 = TrigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
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"model = TrigramNeuralLanguageModel(vocab_size, embed_size, hidden_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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"optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\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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@ -132,9 +134,9 @@
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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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" print(f'steps: {step}, loss: {loss.item()}')\n",
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" if step != 0:\n",
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" torch.save(model.state_dict(), f'trigram_nn_model_steps-{step}_vocab-{vocab_size}_embed-{embed_size}_batch-{batch_size}_hidden-{hidden_size}_lr-{learning_rate}.bin')\n",
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" loss.backward()\n",
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" optimizer.step()\n",
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" if step == max_steps:\n",
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@ -150,12 +152,11 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 9,
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"outputs": [],
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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_size = 20000\n",
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"embed_size = 150\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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@ -165,44 +166,58 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"execution_count": 11,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"trigram_nn_model_steps-13000_vocab-20000_embed-150_batch-512_hidden-256_lr-0.0001.bin\n",
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"512\n",
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"256\n",
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"trigram_nn_model_steps-7000_vocab-20000_embed-150_batch-1024_hidden-1024_lr-0.001.bin\n",
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"1024\n",
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"1024\n",
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"trigram_nn_model_steps-6000_vocab-20000_embed-150_batch-4096_hidden-256_lr-0.001.bin\n",
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"4096\n",
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"256\n"
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]
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}
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],
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"source": [
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"for model_name in ['model_steps-1000_vocab-5000_embed-50_batch-5000.bin',\n",
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" 'model_steps-1000_vocab-5000_embed-50_batch-5000.bin', 'model_steps-27000_vocab-5000_embed-50_batch-5000.bin']:\n",
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"for model_name in ['trigram_nn_model_steps-13000_vocab-20000_embed-150_batch-512_hidden-256_lr-0.0001.bin', 'trigram_nn_model_steps-7000_vocab-20000_embed-150_batch-1024_hidden-1024_lr-0.001.bin', 'trigram_nn_model_steps-6000_vocab-20000_embed-150_batch-4096_hidden-256_lr-0.001.bin']:\n",
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" print(model_name)\n",
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" batch_size = int(model_name.split('_')[-3].split('-')[1])\n",
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" print(batch_size)\n",
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" hidden_size = int(model_name.split('_')[-2].split('-')[1])\n",
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" print(hidden_size)\n",
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" topk = 10\n",
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" preds = []\n",
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" device = 'cuda'\n",
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" model = TrigramNeuralLanguageModel(vocab_size, embed_size).to(device)\n",
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" model = TrigramNeuralLanguageModel(vocab_size, embed_size, hidden_size).to(device)\n",
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" model.load_state_dict(torch.load(model_name))\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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" right_context = simple_preprocess(line.decode('utf-8').split('\\t')[-1]).split()[:2]\n",
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" x = torch.tensor(vocab.forward([right_context[0]])).to(device), \\\n",
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" torch.tensor(vocab.forward([right_context[1]])).to(device)\n",
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" out = model(x)\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\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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" right_context = simple_preprocess(line.decode('utf-8').split('\\t')[-1].strip()).split()[:2]\n",
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" x = torch.tensor(vocab.forward([right_context[0]])).to(device), \\\n",
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" torch.tensor(vocab.forward([right_context[1]])).to(device)\n",
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" out = model(x)\n",
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" top = torch.topk(out[0], topk)\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 = zip(top_words, top_probs)\n",
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" pred = ''\n",
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" total_prob = 0\n",
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" for word, prob in top_zipped:\n",
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" if word != '<unk>':\n",
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" pred += f'{word}:{prob} '\n",
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" total_prob += prob\n",
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" unk_prob = 1 - total_prob\n",
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" pred += f':{unk_prob}'\n",
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" f_out.write(pred + '\\n')\n",
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" src=f'{path}/out.tsv'\n",
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" dst=f\"{path}/{model_name.split('.')[0]}_out.tsv\"\n",
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" shutil.copy(src, dst)"
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@ -210,6 +225,38 @@
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"/home/ked/PycharmProjects/mj9/challenging-america-word-gap-prediction\n",
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"300.66\r\n",
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"/home/ked/PycharmProjects/mj9\n"
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]
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}
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],
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"source": [
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"%cd challenging-america-word-gap-prediction/\n",
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"!./geval --test-name dev-0\n",
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"%cd ../"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"outputs": [],
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"source": [],
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"metadata": {
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"collapsed": false
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}
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}
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],
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"metadata": {
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14828
test-A/out.tsv
14828
test-A/out.tsv
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Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
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