trigram nn
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@ -130,11 +130,11 @@
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"execution_count": 7,
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"outputs": [],
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"source": [
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"def prediction(words, model) -> str:\n",
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"def prediction(words, model, top) -> str:\n",
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" words_tensor = [train_dataset.vocab.forward([word]) for word in words]\n",
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" ixs = torch.tensor(words_tensor).view(-1).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 = torch.topk(out[0], top)\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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@ -158,14 +158,14 @@
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"execution_count": 8,
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"outputs": [],
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"source": [
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"def create_outputs(folder_name, model):\n",
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"def create_outputs(folder_name, model, top):\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}/out-EMBED_SIZE={embed_size}.tsv', 'w', encoding='utf-8', newline='\\n') as f:\n",
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" with open(f'{folder_name}/out-top={top}.tsv', 'w', encoding='utf-8', newline='\\n') as f:\n",
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" for line in fid:\n",
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" separated = line.split('\\t')\n",
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" prefix = separated[6].replace(r'\\n', ' ').split()[-2:]\n",
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" output_line = prediction(prefix, model)\n",
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" output_line = prediction(prefix, model, top)\n",
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" f.write(output_line + '\\n')"
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],
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"metadata": {
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@ -177,10 +177,10 @@
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"execution_count": 9,
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"outputs": [],
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"source": [
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"def train_model():\n",
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"def train_model(lr):\n",
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" model = SimpleTrigramNeuralLanguageModel(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(), lr=0.0001)\n",
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" optimizer = torch.optim.Adam(model.parameters(), lr=lr)\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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@ -215,16 +215,13 @@
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"outputs": [],
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"source": [
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"def with_hyperparams():\n",
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" for e_size in [200, 300]:\n",
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" global embed_size\n",
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" embed_size = e_size\n",
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" train_model()\n",
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" train_model(lr=0.0001)\n",
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" model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size, hidden_size).to(device)\n",
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" model.load_state_dict(torch.load(path_to_model))\n",
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" model.eval()\n",
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"\n",
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" create_outputs('dev-0', model)\n",
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" create_outputs('test-A', model)"
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" for top in [200, 400, 600]:\n",
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" create_outputs('dev-0', model, top)\n",
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" create_outputs('test-A', model, top)"
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],
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"metadata": {
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"id": "kdjy-pX9dzWX"
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@ -314,9 +311,9 @@
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"execution_count": 13,
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"outputs": [],
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"source": [
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"vocab_size = 30000\n",
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"embed_size = 200\n",
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"hidden_size = 500\n",
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"vocab_size = 25000\n",
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"embed_size = 300\n",
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"hidden_size = 150\n",
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"batch_size = 2000\n",
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"device = 'cuda'\n",
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"path_to_train = 'train/in.tsv.xz'\n",
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description: nn trigram multiple outs
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tags:
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- neural-network
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- ngram
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- trigram
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params:
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epochs: 1
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learning-rate: 0.0001
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vocab_size: 25000
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embed_size: 300
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hidden_size: 150
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batch_size: 2000
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unwanted-params:
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- model-file
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- vocab-file
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