challenging-america-word-gap-prediction/trigram_neural.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"machine_shape":"hm","gpuType":"V100","authorship_tag":"ABX9TyNxnyqaLopDiv3Mni0cDb0b"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU","gpuClass":"standard"},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"8Ov1POzi2VY9"},"outputs":[],"source":[]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8SY0Ca7ueoZB","executionInfo":{"status":"ok","timestamp":1683659918042,"user_tz":-120,"elapsed":28484,"user":{"displayName":"Martyna Drumińska","userId":"13361003509289187965"}},"outputId":"040c3f2c-7da1-49f2-a8a0-75263b2539e8"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"X5yAfFNG2ijo"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["cd drive/MyDrive"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sB_SM-dbe45A","executionInfo":{"status":"ok","timestamp":1683660270671,"user_tz":-120,"elapsed":250,"user":{"displayName":"Martyna Drumińska","userId":"13361003509289187965"}},"outputId":"19dd8ff3-7788-473b-fbec-1edf2c048f2a"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive\n"]}]},{"cell_type":"code","source":["cd challenging-america-word-gap-prediction/"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GTzGNc2c2mSy","executionInfo":{"status":"ok","timestamp":1683660271700,"user_tz":-120,"elapsed":4,"user":{"displayName":"Martyna Drumińska","userId":"13361003509289187965"}},"outputId":"f9d39452-f209-4b22-cd96-237a5a468c0e"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/drive/MyDrive/challenging-america-word-gap-prediction\n"]}]},{"cell_type":"code","source":["import itertools\n","import lzma\n","import numpy as np\n","import regex as re\n","import torch\n","import pandas as pd\n","from torch import nn\n","from torch.utils.data import IterableDataset, DataLoader\n","import csv\n","from itertools import islice, chain\n","from torchtext.vocab import build_vocab_from_iterator"],"metadata":{"id":"AQBLNJJH2pfP","executionInfo":{"status":"ok","timestamp":1683662202333,"user_tz":-120,"elapsed":249,"user":{"displayName":"Martyna Drumińska","userId":"13361003509289187965"}}},"execution_count":20,"outputs":[]},{"cell_type":"code","source":["def clean_text(txt):\n","    txt = txt.lower().replace('-\\\\\\\\\\\\\\\\n', '').replace('\\\\\\\\\\\\\\\\n', ' ')\n","    txt = re.sub(r'\\p{P}', '', txt)\n","    txt = txt.replace(\"'t\", \" not\").replace(\"'s\", \" is\").replace(\"'ll\", \" will\").replace(\"'m\", \" am\").replace(\"'ve\", \" have\")\n","    txt = txt.replace(\"’\", \"'\")\n","    txt = txt.replace(\" this\\\\nplace\", \"this place\")\n","    txt = txt.replace(\"'we\\\\nwere\", \"we were\")\n","    txt = txt.replace(\"'ever\\\\nwas\", \"ever was\")\n","    txt = txt.replace(\"'making\\\\nsuch\", \"making such\")\n","    txt = txt.replace(\"'boot\\\\nto\", \"boot to\")\n","    txt = txt.replace(\"'elsewhere\\\\nfrom\", \"elsewhere from\")\n","    txt=txt.replace(\"United\\\\nStates\",\"United States\")\n","    txt = txt.replace(\"Unit-\\\\ned\",\"United\" )\n","    txt = txt.replace(\"neigh-\\\\nbors\", \"neighbours\")\n","    txt = txt.replace(\"aver-\\\\nage\", \"average\")\n","    txt = txt.replace(\"people\\\\ndown\", \"people down\")\n","    txt =re.compile(r\"'s|[\\-]|\\-\\\\n|\\p{P}\").sub(\"\", txt)\n","    txt = re.compile(r\"[{}\\[\\]\\&%^$*#\\(\\)@\\t\\n0123456789]+\").sub(\" \", txt)\n","\n","    return txt"],"metadata":{"id":"mGsDsF8a6Amp","executionInfo":{"status":"ok","timestamp":1683660292442,"user_tz":-120,"elapsed":233,"user":{"displayName":"Martyna Drumińska","userId":"13361003509289187965"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["device='cuda'"],"metadata":{"id":"K7eEHH4cAlfs","executionInfo":{"status":"ok","timestamp":1683660900600,"user_tz":-120,"elapsed":356,"user":{"displayName":"Martyna Drumińska","userId":"13361003509289187965"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["train_data = pd.read_csv('train/in.tsv.xz', sep='\\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)\n","train_labels = pd.read_csv('train/expected.tsv', sep='\\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)\n","train_data = train_data[[6, 7]]\n","train_data = pd.concat([train_data, train_labels], axis=1)\n","train_data['text'] = train_data[6] + train_data[0] + train_data[7]\n","train_data = train_data[['text']]\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GlhRbeyx5gFi","executionInfo":{"status":"ok","timestamp":1683660450564,"user_tz":-120,"elapsed":33583,"user":{"displayName":"Martyna Drumińska","userId":"13361003509289187965"}},"outputId":"6e975362-fb81-4404-a9c5-1df280bd56f8"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-6-2673b85efcb9>:1: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n","\n","\n","  train_data = pd.read_csv('train/in.tsv.xz', sep='\\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)\n","<ipython-input-6-2673b85efcb9>:1: FutureWarning: The warn_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n","\n","\n","  train_data = pd.read_csv('train/in.tsv.xz', sep='\\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)\n","<ipython-input-6-2673b85efcb9>:2: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n","\n","\n","  train_labels = pd.read_csv('train/expected.tsv', sep='\\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)\n","<ipython-input-6-2673b85efcb9>:2: FutureWarning: The warn_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n","\n","\n","  train_labels = pd.read_csv('train/expected.tsv', sep='\\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)\n"]}]},{"cell_type":"code","source":["train_data"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"GphrVDQR7Adc","executionInfo":{"status":"ok","timestamp":1683659482140,"user_tz":-120,"elapsed":253,"user":{"displayName":"Martyna Drumińska","userId":"13361003509289187965"}},"outputId":"e7ae62b0-160b-4f53-fae3-371732083ec6"},"execution_count":16,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                     text\n","0       came fiom the last place to this\\nplace, and t...\n","1       MB. BOOT'S POLITICAL OBEED\\nAttempt to imagine...\n","2       \"Thera were in 1771 only aeventy-nine\\n*ub*erl...\n","3       A gixnl man y nitereRtiiiv dii-clos-\\nur«s reg...\n","4       Tin: 188UB TV THF BBABBT QABJE\\nMr. Schiffs *t...\n","...                                                   ...\n","432017  Sam Clendenin bad a fancy for Ui«\\nscience of ...\n","432018  Wita.htt halting the party ware dilven to the ...\n","432019  It was the last thing that either of\\nthem exp...\n","432020  settlement with the department.\\nIt is also sh...\n","432021  Flour quotations—low extras at 1 R0®2 50;\\ncit...\n","\n","[432022 rows x 1 columns]"],"text/html":["\n","  <div id=\"df-88fb9255-21be-4dbf-bb5d-2b14036f9903\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>text</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>came fiom the last place to this\\nplace, and t...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>MB. BOOT'S POLITICAL OBEED\\nAttempt to imagine...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>\"Thera were in 1771 only aeventy-nine\\n*ub*erl...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>A gixnl man y nitereRtiiiv dii-clos-\\nur«s reg...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Tin: 188UB TV THF BBABBT QABJE\\nMr. Schiffs *t...</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>432017</th>\n","      <td>Sam Clendenin bad a fancy for Ui«\\nscience of ...</td>\n","    </tr>\n","    <tr>\n","      <th>432018</th>\n","      <td>Wita.htt halting the party ware dilven to the ...</td>\n","    </tr>\n","    <tr>\n","      <th>432019</th>\n","      <td>It was the last thing that either of\\nthem exp...</td>\n","    </tr>\n","    <tr>\n","      <th>432020</th>\n","      <td>settlement with the department.\\nIt is also sh...</td>\n","    </tr>\n","    <tr>\n","      <th>432021</th>\n","      <td>Flour quotations—low extras at 1 R0®2 50;\\ncit...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>432022 rows × 1 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-88fb9255-21be-4dbf-bb5d-2b14036f9903')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-88fb9255-21be-4dbf-bb5d-2b14036f9903 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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'\\n')"],"metadata":{"id":"hWG3y3fy7ETJ","executionInfo":{"status":"ok","timestamp":1683660563661,"user_tz":-120,"elapsed":113110,"user":{"displayName":"Martyna Drumińska","userId":"13361003509289187965"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["\n","class SimpleTrigramNeuralLanguageModel(nn.Module):\n","    def __init__(self, vocabulary_size, embedding_size, hidden_size):\n","        super(SimpleTrigramNeuralLanguageModel, self).__init__()\n","        self.embedding = nn.Embedding(vocabulary_size * 2, embedding_size)\n","        self.linear1 = nn.Linear(embedding_size, hidden_size)\n","        self.linear2 = nn.Linear(hidden_size, vocabulary_size * 2)\n","\n","    def forward(self, x):\n","        x = self.embedding(x)\n","        x = self.linear1(x)\n","        x = self.linear2(x)\n","        x = torch.softmax(x, dim=1)\n","        return x"],"metadata":{"id":"WxZXVehY7TZc","executionInfo":{"status":"ok","timestamp":1683660563662,"user_tz":-120,"elapsed":27,"user":{"displayName":"Martyna Drumińska","userId":"13361003509289187965"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["vocab_size = 38000\n","embed_size = 300\n","hidden_size = 128"],"metadata":{"id":"LcnXO3fT_CVS","executionInfo":{"status":"ok","timestamp":1683661947512,"user_tz":-120,"elapsed":219,"user":{"displayName":"Martyna Drumińska","userId":"13361003509289187965"}}},"execution_count":17,"outputs":[]},{"cell_type":"code","source":["def words_line(line):\n","    line = line.rstrip()\n","    yield '<s>'\n","    for m in re.finditer(r'[\\p{L}0-9\\*]+|\\p{P}+', line):\n","        yield m.group(0).lower()\n","    yield '</s>'\n","\n","def file_words(file_name):\n","    with open(file_name, 'r', encoding='utf-8') as fh:\n","        for line in fh:\n","            yield words_line(line)"],"metadata":{"id":"wdgnc1QIB9Ju","executionInfo":{"status":"ok","timestamp":1683661253786,"user_tz":-120,"elapsed":219,"user":{"displayName":"Martyna Drumińska","userId":"13361003509289187965"}}},"execution_count":12,"outputs":[]},{"cell_type":"code","source":["def iterator_look(gen):\n","    first_prev = None\n","    sec_prev = None\n","    for item in gen:\n","        if first_prev and sec_prev:\n","            yield (sec_prev+ first_prev, item)\n","        sec_prev = first_prev\n","        first_prev = item"],"metadata":{"id":"fApeAZfr7UF-","executionInfo":{"status":"ok","timestamp":1683661625401,"user_tz":-120,"elapsed":235,"user":{"displayName":"Martyna Drumińska","userId":"13361003509289187965"}}},"execution_count":15,"outputs":[]},{"cell_type":"code","source":["class Trigrams(IterableDataset):\n","    def __init__(self, text_file, vocabulary_size):\n","        self.vocab = build_vocab_from_iterator(\n","            file_words(text_file),\n","            max_tokens = vocabulary_size,\n","            specials = ['<unk>']\n","        )\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 iterator_look((self.vocab[t] for t in chain.from_iterable(file_words(self.text_file))))"],"metadata":{"id":"NP9xRS8V_o_K","executionInfo":{"status":"ok","timestamp":1683661627830,"user_tz":-120,"elapsed":8,"user":{"displayName":"Martyna Drumińska","userId":"13361003509289187965"}}},"execution_count":16,"outputs":[]},{"cell_type":"code","source":["def training(xx):\n","  train_dataset_new = Trigrams('train_new.txt', vocab_size)\n","  model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size, hidden_size).to(device)\n","  optimizer = torch.optim.Adam(model.parameters())\n","  criterion = torch.nn.NLLLoss()\n","  data = DataLoader(train_dataset_new, batch_size=800)\n","  step = 0\n","  for epoch in range(2):\n","      model.train()\n","      for x, y in data:\n","          x = x.to(device)\n","          y = y.to(device)\n","          optimizer.zero_grad()\n","          outputs = model(x)\n","          loss = criterion(torch.log(outputs), y)\n","          if step % 100 == 0:\n","              print(step, loss)\n","          step += 1\n","          loss.backward()\n","          optimizer.step()\n","  torch.save(model.state_dict(), 'model/model1.bin')"],"metadata":{"id":"xTVB0zZJ7qZk","executionInfo":{"status":"ok","timestamp":1683662248933,"user_tz":-120,"elapsed":217,"user":{"displayName":"Martyna Drumińska","userId":"13361003509289187965"}}},"execution_count":22,"outputs":[]},{"cell_type":"code","source":["training(xx=0.0001)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZvkhRfeXE0Yf","executionInfo":{"status":"ok","timestamp":1683668268966,"user_tz":-120,"elapsed":2822540,"user":{"displayName":"Martyna Drumińska","userId":"13361003509289187965"}},"outputId":"1fba0732-773c-4207-926e-5ecefa706b8f"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["0 tensor(11.2779, device='cuda:0', grad_fn=<NllLossBackward0>)\n","100 tensor(8.7151, device='cuda:0', grad_fn=<NllLossBackward0>)\n","200 tensor(6.9910, device='cuda:0', grad_fn=<NllLossBackward0>)\n","300 tensor(6.5174, device='cuda:0', grad_fn=<NllLossBackward0>)\n","400 tensor(6.6716, device='cuda:0', grad_fn=<NllLossBackward0>)\n","500 tensor(6.5920, device='cuda:0', grad_fn=<NllLossBackward0>)\n","600 tensor(6.6799, device='cuda:0', grad_fn=<NllLossBackward0>)\n","700 tensor(6.7216, device='cuda:0', grad_fn=<NllLossBackward0>)\n","800 tensor(6.4623, device='cuda:0', grad_fn=<NllLossBackward0>)\n","900 tensor(6.5840, device='cuda:0', grad_fn=<NllLossBackward0>)\n","1000 tensor(6.3399, device='cuda:0', grad_fn=<NllLossBackward0>)\n","1100 tensor(6.5420, device='cuda:0', grad_fn=<NllLossBackward0>)\n","1200 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grad_fn=<NllLossBackward0>)\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"EjcVpPljf9LI"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size, hidden_size).to(device)\n","model.load_state_dict(torch.load('model/model1.bin'))\n","model.eval()\n","train_dataset_new = Trigrams('train_new.txt', vocab_size)\n","\n","def predict_words(words, top):\n","    ixs = torch.tensor(train_dataset_new.vocab.forward(['with'])).to(device)\n","    predictions = model(ixs)\n","    total_prob = 0.0\n","    prediction = ''\n","    top = torch.topk(predictions[0], 30)\n","    top_indices = top.indices.tolist()\n","    top_probs = top.values.tolist()\n","    top_words = train_dataset_new.vocab.lookup_tokens(top_indices)\n","    top_preds = list(zip(top_words, top_indices, top_probs))\n","\n","    for word, _, prob in top_preds:\n","        if word != '<unk>':\n","            prediction += f'{word}:{prob} '\n","            total_prob += prob\n","    prediction += f':{1 - total_prob}'\n","    return prediction"],"metadata":{"id":"g2oFEUu9APZv","executionInfo":{"status":"ok","timestamp":1683669530478,"user_tz":-120,"elapsed":150202,"user":{"displayName":"Martyna Drumińska","userId":"13361003509289187965"}}},"execution_count":33,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"YS-2PZtIAN60"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","model = SimpleTrigramNeuralLanguageModel(vocab_size, embed_size, hidden_size).to(device)\n","model.load_state_dict(torch.load('model/model1.bin'))\n","model.eval() "],"metadata":{"id":"P-CCdOm2EK_I"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["for x in [100, 200, 1000]:\n","  with lzma.open(f'test-A/in.tsv.xz', mode='rt', encoding='utf-8') as fid:\n","        with open(f'test-A/out-HIDDENLAYER={x}.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()[-2:]\n","                output_line = predict_words(prefix, x)\n","                f.write(output_line + '\\n')"],"metadata":{"id":"CwT7lkECGnDr","executionInfo":{"status":"ok","timestamp":1683669772607,"user_tz":-120,"elapsed":15191,"user":{"displayName":"Martyna Drumińska","userId":"13361003509289187965"}}},"execution_count":34,"outputs":[]},{"cell_type":"code","source":["for x in [100, 200, 1000]:\n","  with lzma.open(f'dev-0/in.tsv.xz', mode='rt', encoding='utf-8') as fid:\n","        with open(f'dev-0/out-HIDDENLAYER={x}.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()[-2:]\n","                output_line = predict_words(prefix, x)\n","                f.write(output_line + '\\n')"],"metadata":{"id":"23rB2anmHK7y","executionInfo":{"status":"ok","timestamp":1683669807961,"user_tz":-120,"elapsed":21897,"user":{"displayName":"Martyna Drumińska","userId":"13361003509289187965"}}},"execution_count":36,"outputs":[]},{"cell_type":"code","source":["torch.save(model.state_dict(), 'model/model1.bin')"],"metadata":{"id":"WSPMRx217xMp"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"xt-q0hCq7xwT"},"execution_count":null,"outputs":[]}]}