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Sun","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15727073160566641365"}},"outputId":"84561e30-b303-436b-a026-a1beeef27314"},"source":["!pip install sentence_transformers\n","!pip install transformers"],"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting sentence_transformers\n","  Downloading sentence-transformers-2.1.0.tar.gz (78 kB)\n","\u001b[K     |████████████████████████████████| 78 kB 3.8 MB/s \n","\u001b[?25hCollecting transformers<5.0.0,>=4.6.0\n","  Downloading transformers-4.12.5-py3-none-any.whl (3.1 MB)\n","\u001b[K     |████████████████████████████████| 3.1 MB 32.6 MB/s \n","\u001b[?25hCollecting tokenizers>=0.10.3\n","  Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n","\u001b[K     |████████████████████████████████| 3.3 MB 53.9 MB/s \n","\u001b[?25hRequirement already satisfied: tqdm in 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|████████████████████████████████| 1.2 MB 54.5 MB/s \n","\u001b[?25hCollecting huggingface-hub\n","  Downloading huggingface_hub-0.2.1-py3-none-any.whl (61 kB)\n","\u001b[K     |████████████████████████████████| 61 kB 606 kB/s \n","\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.6.0->sentence_transformers) (3.10.0.2)\n","Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence_transformers) (4.8.2)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence_transformers) (21.3)\n","Collecting pyyaml>=5.1\n","  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n","\u001b[K     |████████████████████████████████| 596 kB 61.3 MB/s \n","\u001b[?25hCollecting sacremoses\n","  Downloading sacremoses-0.0.46-py3-none-any.whl (895 kB)\n","\u001b[K     |████████████████████████████████| 895 kB 75.5 MB/s \n","\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence_transformers) (2019.12.20)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence_transformers) (3.4.0)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers<5.0.0,>=4.6.0->sentence_transformers) (2.23.0)\n","Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers<5.0.0,>=4.6.0->sentence_transformers) (3.0.6)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers<5.0.0,>=4.6.0->sentence_transformers) (3.6.0)\n","Requirement already satisfied: six in 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transformers, sentencepiece, sentence-transformers\n","  Attempting uninstall: pyyaml\n","    Found existing installation: PyYAML 3.13\n","    Uninstalling PyYAML-3.13:\n","      Successfully uninstalled PyYAML-3.13\n","Successfully installed huggingface-hub-0.2.1 pyyaml-6.0 sacremoses-0.0.46 sentence-transformers-2.1.0 sentencepiece-0.1.96 tokenizers-0.10.3 transformers-4.12.5\n","Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.12.5)\n","Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers) (0.0.46)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)\n","Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.10.3)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n","Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.8.2)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.4.0)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.62.3)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.2.1)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (3.10.0.2)\n","Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.6)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.6.0)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.10.8)\n","Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n","Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.1.0)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"jaU9Yet2HOca","executionInfo":{"status":"ok","timestamp":1638849506386,"user_tz":300,"elapsed":29563,"user":{"displayName":"Jie Sun","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15727073160566641365"}},"outputId":"2439cef4-aa44-4df9-d07a-c0b6747c6d3e"},"source":["from google.colab import drive\n","drive.mount('/content/gdrive') # for google colab. adjust accordingly\n","\n","PARENT_DIR = '/content/gdrive/MyDrive/CS287_Research_Project/Jennas_Code/' # for google colab. adjust accordingly\n","\n","import sys\n","sys.path.append(PARENT_DIR)\\\n","\n","!pip install spacy\n","!python -m spacy download de_core_news_sm\n","\n","import sys  \n","sys.path.insert(0, PARENT_DIR)"],"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive\n","Requirement already satisfied: spacy in /usr/local/lib/python3.7/dist-packages (2.2.4)\n","Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (2.23.0)\n","Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy) (3.0.6)\n","Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.0.6)\n","Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.1.3)\n","Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.0.5)\n","Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy) (57.4.0)\n","Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (7.4.0)\n","Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy) (2.0.6)\n","Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.0.0)\n","Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (4.62.3)\n","Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.19.5)\n","Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (0.4.1)\n","Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (0.8.2)\n","Requirement already satisfied: importlib-metadata>=0.20 in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy) (4.8.2)\n","Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20->catalogue<1.1.0,>=0.0.7->spacy) (3.10.0.2)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20->catalogue<1.1.0,>=0.0.7->spacy) (3.6.0)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2.10)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (1.24.3)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (3.0.4)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2021.10.8)\n","Collecting de_core_news_sm==2.2.5\n","  Downloading https://github.com/explosion/spacy-models/releases/download/de_core_news_sm-2.2.5/de_core_news_sm-2.2.5.tar.gz (14.9 MB)\n","\u001b[K     |████████████████████████████████| 14.9 MB 5.1 MB/s \n","\u001b[?25hRequirement already satisfied: spacy>=2.2.2 in /usr/local/lib/python3.7/dist-packages (from de_core_news_sm==2.2.5) (2.2.4)\n","Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (0.8.2)\n","Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (1.0.5)\n","Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (1.1.3)\n","Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (1.19.5)\n","Requirement already satisfied: setuptools in 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(2.0.6)\n","Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (7.4.0)\n","Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.2.2->de_core_news_sm==2.2.5) (2.23.0)\n","Requirement already satisfied: importlib-metadata>=0.20 in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->de_core_news_sm==2.2.5) (4.8.2)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->de_core_news_sm==2.2.5) (3.6.0)\n","Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->de_core_news_sm==2.2.5) (3.10.0.2)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->de_core_news_sm==2.2.5) (2.10)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->de_core_news_sm==2.2.5) (3.0.4)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->de_core_news_sm==2.2.5) (1.24.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->de_core_news_sm==2.2.5) (2021.10.8)\n","Building wheels for collected packages: de-core-news-sm\n","  Building wheel for de-core-news-sm (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for de-core-news-sm: filename=de_core_news_sm-2.2.5-py3-none-any.whl size=14907055 sha256=6f3fb8b89f02798450810a3be5d6c941c22bbcfcd2a630f768c377d6bbd87f65\n","  Stored in directory: /tmp/pip-ephem-wheel-cache-7dq5jkip/wheels/00/66/69/cb6c921610087d2cab339062345098e30a5ceb665360e7b32a\n","Successfully built de-core-news-sm\n","Installing collected packages: de-core-news-sm\n","Successfully installed de-core-news-sm-2.2.5\n","\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n","You can now load the model via spacy.load('de_core_news_sm')\n"]}]},{"cell_type":"code","metadata":{"id":"jUKDeGZLTkNU","executionInfo":{"status":"ok","timestamp":1638838005397,"user_tz":300,"elapsed":11281,"user":{"displayName":"Jie Sun","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15727073160566641365"}}},"source":["from similarity_load_data import *"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"id":"n778lvHNHgEz","executionInfo":{"status":"ok","timestamp":1638838010273,"user_tz":300,"elapsed":4880,"user":{"displayName":"Jie Sun","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15727073160566641365"}}},"source":["_, mt_val_ds, mt_test_ds, mt_train_dl, mt_val_dl, mt_test_dl = load_pickled_dataloaders()"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"SoaipQcyM6cr","executionInfo":{"status":"ok","timestamp":1638838017803,"user_tz":300,"elapsed":7534,"user":{"displayName":"Jie Sun","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15727073160566641365"}}},"source":["with open(PARENT_DIR+'data/dataloaders10perc_batchsize32/mt_train_ds_tokenize2.pickle', 'rb') as handle:\n","    mt_train_ds = pickle.load(handle)\n","\n","with open(PARENT_DIR+'data/dataloaders10perc_batchsize32/mt_train_dl_tokenize2.pickle', 'rb') as handle:\n","    mt_train_dl = pickle.load(handle)"],"execution_count":5,"outputs":[]},{"cell_type":"code","metadata":{"id":"msr_nw9Ot8Ca","executionInfo":{"status":"ok","timestamp":1638838017804,"user_tz":300,"elapsed":8,"user":{"displayName":"Jie Sun","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15727073160566641365"}}},"source":["iterator = iter(mt_train_ds)\n"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iIepC5IKOXPQ","executionInfo":{"status":"ok","timestamp":1638549648556,"user_tz":300,"elapsed":18,"user":{"displayName":"Jie Sun","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15727073160566641365"}},"outputId":"68f06141-c386-478d-ee3c-cb7ce603ef15"},"source":["next(iterator)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(tensor([   2,   29,  274,   12,    0,   23, 3934,   14,  103,    3]),\n"," tensor([   2,   23,   16,    4,  968,    7,  392, 4354,   11, 2905,    3]),\n"," tensor([0, 1, 2, 1, 3, 4, 5, 6, 2, 0]),\n"," tensor([0, 1, 2, 7, 5, 4, 2, 5, 4, 8, 0]),\n"," 'was bedeutet es menü auf arabisch zu sagen')"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"y-Es4XlLOYee","executionInfo":{"status":"ok","timestamp":1638549648557,"user_tz":300,"elapsed":6,"user":{"displayName":"Jie Sun","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15727073160566641365"}},"outputId":"63940d49-ae49-431b-c99b-a9ce51337561"},"source":["next(iterator)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(tensor([   2,   24,  123, 3510,    0,    0,    9,   47,   13,    0,   10,   48,\n","           15,  583,    0,   35, 2620,  516,   37,   42,  549, 2621,   17, 4537,\n","           14, 1673,    3]),\n"," tensor([   2,   25,   12,  482,   59,   36, 2906,    0,    0,   64,   16,   71,\n","           33,    0,   85,  809,    8,  109,  298,    7, 1841,   21, 1354, 1221,\n","           45,  228,    3]),\n"," tensor([ 0,  9,  7,  5,  8,  8,  7, 10,  4,  8, 11, 11,  7,  3,  5,  4,  5,  2,\n","          4,  7,  5,  4,  4,  5,  6,  2,  0]),\n"," tensor([ 0,  9,  1,  2,  4,  7,  5,  8,  8,  1,  2, 10,  4,  8, 11,  2,  7,  3,\n","          5,  4,  5,  4,  2,  5,  4,  5,  0]),\n"," 'aber mein kollege pieter abbeel der hier in berkeley ist hat ein neues set an techniken entwickelt um einen roboter anhand von beispielen zu unterrichten')"]},"metadata":{},"execution_count":8}]},{"cell_type":"code","metadata":{"id":"6f-wVEY3KPct"},"source":["# with open(PARENT_DIR+'data/dataloaders10perc_batchsize32/mt_train_ds_tokenize2.pickle', 'wb') as handle:\n","#     pickle.dump(mt_train_ds, handle, protocol=pickle.HIGHEST_PROTOCOL)\n","\n","# with open(PARENT_DIR+'data/dataloaders10perc_batchsize32/mt_train_dl_tokenize2.pickle', 'wb') as handle:\n","#     pickle.dump(mt_train_dl, handle, protocol=pickle.HIGHEST_PROTOCOL)\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Mc0p-VpYOEKv"},"source":["  # train_dict, val_dict, test_dict = load_dictionaries(small = True)\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":343,"referenced_widgets":["ed416c752298467bba33cd4eaf51ef8a","a19b861c865440a482f9abc64be65022","a5e96baaa9014439a6d47522ee59ab8b","25151e4432fe47c09a074be434733bd1","967f617dcb4241e18db6f6e48f69ce36","dbce6afff4744c1299d2c8ecfeb99b44","f029f5a98a8349bf88dae14dba6b6d29","bfefd6876b144bbba190c0b3fcb65467","9ba851eaae3145128da87345e29a4c33","77c092ed560d44a3a4ca9dcf805d90b9","823e7b9113184f5c8dda77deaa06ee02","8e039dfdc3e14ac69f9c323d15b46580","4a2a38bdd968473e810d0fddae7b7b32","58108ac58df442289fa4dccf5b603db5","5c9e8678a40047e991045877111f8f93","d2c228d1101341d4b8a5843cea5421eb","178a530fee7947bebbb01c80275db3b3","2253a8c0fc194dfc891d77b52ed16585","7f4ef8f448a54a0181a40397e083bffc","727a51eeebe24584b61b58e7468ab696","096834ffb43b43d6b75f71b9626a7fbb","bcb35e89e0cd4ef29000e35b69877d80","88ac5e3e1cfa4b6b9a0510c801ff989b","5f039e7345ba4be4b21042c1c69ea0f1","99e5208e578f4c4b8a76cbd7dc2aa234","2ae94e2bbd164b59992f3d560bc0f94d","a11ad6ee4817423e8ffde3f325e05039","f46e689018254b219a2c29cb1ae064d5","6b2627869f9943ccb01ecfe60b9c53ff","2ed175656c534fdf962cb6923f219600","9342a2c1988b44bf96e3e458920cc93d","176b19da48f94a0ba2fccbdb114d798f","f21e3af3e437436590d6ac08867c5908","5e314a29b2094c1094f2cda96f87977a","3d9963d1731541ccb87c40f14f036dc5","ccbebc1319524fc6ae93a485954abed0","a874068e33fb4b2aa1fe2af241ff4f6d","a0961932484e41ed8a7c09e8c6872bb8","7a30435b9db544eabcff53c2f1ace4f4","8e2ed79a96114d52b635cacefd1e909d","c09c657c243c4a8f8d9abba7992852a6","e87a0ad5d8404280ab4c50c292934fd4","1c0d42314ea340c4a9075903e5f2c438","c6176f8e357b493584e91b8acb5567a0","8b336153c4924edba716e704a5d6832f","ee4c940aa6544645acf19108c7401ac3","cc5a740bb219424487bab71dd8d0513c","8489223fb13c488cbd0e9456f4e91749","6f29d7d3e78b4a4b8ff66dc959c29a9c","9b548002744947be861bf3835feb5e87","62cc0a4a4bd7402f84d70370d6a2698c","320b9572f450451ebbc715d0e5065671","20444748313b4ed593c688bee39c5424","90afe99122d44433a809252e476eb604","101cf5bfe7fc457580e51c5d96352563","ac79d3c9f8c34fdea6a87fd70b2c931a","f3ca262d0a3e4c148c1bd9403df056be","0602fc46a4824a37bb532e35b99c8bdb","a554b4bc012440b6a2a159b4f5f39320","5c4ce530b5bc418c94a6f7c477a94068","b3cc6ae6d06b4d99a60f186ba5fcfbd9","125768ada71a42628889170fcc03d1cc","c8eb5ff835d04f8aa23a8dbdfa374c2c","61ec8d5b79fb469586e0cfd5efcb5437","df3210fb8e624cce9b8ce5b78bdb3b1d","bef8dd4acc9a45458542a4db525b0944","fdc3da4817db408da7a191b98abda448","e681bdb27f134680abf5aa6b0d918925","4cdc56fa336e4fdb89ee3351cbedf058","d7ae1ca50b92447d89faf35f3fab2179","6db8eb1628f44bb093a4377afc099871","05ad343c6c5b47d482fd6dfc8b5176d7","405c8b32a2ee4e77b3b5a9d4410c61dd","7ec409c3f36f40049d73637869319574","6da6073db23c4565b69e2f4a7fcf252a","46f10d9868a1449485f75cca6b700699","9923aa98ab914cb9ae5cdb8b30bb4713","9b3681ffe84a4e13a969ade279a372f7","128f3677a246469c8811854d97a04a06","b5e8a7f302614f9b918d1024007bd714","4e9cffb8893f492f8777320c87b6aa87","ef5d583f8256463e98c569a330954df7","2c1f2e5c6f7d41a5b14d19fa3b5b401d","bdb351b29d1c4142b37c2f1c51b12818","459f01ffb3f448299e1864c2cb01c854","4f3ad86d4fd7477e90eaa46435ae75e8","54f33e4364d442aba646c2264c5bc9c3","934060e4fe064f79bf004cce6285455d","14607dae56e04156bb03b505c7f799d8","ceb0abf0bafe43a0aa7760c8def48829","5cec66d17a6f43c0a2b069c9d348dd06","d4f2e23b02ec4a7aa0ce51b1d85f283a","38b89c7ec962449fb740f17ca0ae640d","602ec295c7cc41fbb96558f8c3ed885e","e71b2e8ee046418aaf4ec6158ebaf767","46dab5d3b65643228f4e647c0a9fc580","133f4535b1ba4ca3830712bfc90a7538","e97ba9ff85d6475fa19e39d86ea03fea","9c000ce8331b4aa4aabff3c556d297d4"]},"id":"30qqvBa3GELd","executionInfo":{"status":"ok","timestamp":1638838053224,"user_tz":300,"elapsed":34610,"user":{"displayName":"Jie Sun","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15727073160566641365"}},"outputId":"673e64fb-70ee-42dd-ddbe-d22743b9892a"},"source":["from sentence_transformers import SentenceTransformer\n","model = SentenceTransformer('T-Systems-onsite/cross-en-de-roberta-sentence-transformer')"],"execution_count":7,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ed416c752298467bba33cd4eaf51ef8a","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/391 [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"8e039dfdc3e14ac69f9c323d15b46580","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/1.10k [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"88ac5e3e1cfa4b6b9a0510c801ff989b","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/7.88k [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"5e314a29b2094c1094f2cda96f87977a","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/541 [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"8b336153c4924edba716e704a5d6832f","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/1.11G [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ac79d3c9f8c34fdea6a87fd70b2c931a","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/27.0 [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"fdc3da4817db408da7a191b98abda448","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/5.07M [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"9b3681ffe84a4e13a969ade279a372f7","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/150 [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"14607dae56e04156bb03b505c7f799d8","version_minor":0,"version_major":2},"text/plain":["Downloading:   0%|          | 0.00/188 [00:00<?, ?B/s]"]},"metadata":{}},{"output_type":"stream","name":"stderr","text":["WARNING:root:No sentence-transformers model found with name /root/.cache/torch/sentence_transformers/T-Systems-onsite_cross-en-de-roberta-sentence-transformer. Creating a new one with MEAN pooling.\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9thdDpoda60Q","executionInfo":{"status":"ok","timestamp":1638802053991,"user_tz":300,"elapsed":8,"user":{"displayName":"Jie Sun","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15727073160566641365"}},"outputId":"9389432e-a822-401e-985c-5c9b383594cd"},"source":["model.eval() #check if it's different"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["SentenceTransformer(\n","  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel \n","  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})\n",")"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"H6RJpZqevljR","executionInfo":{"status":"ok","timestamp":1638504170022,"user_tz":300,"elapsed":6,"user":{"displayName":"Jie Sun","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15727073160566641365"}},"outputId":"daf24d12-1e5d-4a71-d149-04abb582aa2a"},"source":["next(iter(mt_train_dl))"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(tensor([[  2,  30,  11,  12,  70, 396,  11,  12,   4,  12, 414,  90, 716,  17,\n","           40,   0, 854,   3]]),\n"," tensor([[   2,   54,   14,   69,   12,   14, 1097,   12,    5,   12,  735,    4,\n","            88,   14,   63,   31,   23,   15,    0,   23,   15,  355,    3]]),\n"," tensor([[ 0, 12,  1,  1,  2,  2,  1,  1,  9,  1,  2,  7,  5,  4,  7,  3,  3,  0]]),\n"," tensor([[ 0, 10,  1,  2,  1,  1,  2,  1,  9,  1,  2,  7,  5,  1,  2,  4,  1, 11,\n","           3,  1, 11,  3,  0]]))"]},"metadata":{},"execution_count":11}]},{"cell_type":"markdown","metadata":{"id":"1SkcyaqKgPXi"},"source":["# Getting hidden states. K = 10"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":246},"id":"5v5EQdNLf0J4","executionInfo":{"status":"error","timestamp":1638570465316,"user_tz":300,"elapsed":13,"user":{"displayName":"Jie Sun","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15727073160566641365"}},"outputId":"b6359415-2376-459a-f2b5-049effac17e6"},"source":["# 2. key: hidden states, value: the corresponding batch\n","h_batch = dict()\n","for b in mt_train_ds:\n","  h = torch.tensor(model.encode(b[-1]))\n","  h_batch[h] = b[:2]\n","  \n","  \n","print('size of this dictionary of hidden states:',len(h_batch))\n"],"execution_count":null,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-1-91707c3561c4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 2. key: hidden states, value: the corresponding batch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mh_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mb\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmt_train_ds\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m   \u001b[0mh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m   \u001b[0mh_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mh\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'mt_train_ds' is not defined"]}]},{"cell_type":"code","metadata":{"id":"L86Dr_knqS-W"},"source":["print('size of this dictionary of hidden states:',len(h_batch))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"LhiRwMlyi57l"},"source":["# 3. do Kmeans.\n","K_value = 10\n","\n","import numpy as np\n","from sklearn.cluster import KMeans\n","X = [hs.tolist() for hs in h_batch.keys()]\n","X1 = np.array(X)\n","kmeans = KMeans(n_clusters = K_value, random_state = 0).fit(X1)\n","lab = kmeans.labels_\n","print(lab)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"zJSO096Sf1K_"},"source":["# 4. create another dictionary to hold labels with each batch (row)\n","# key: batch (t1,t2), which is the value of h_batch, value: labels\n","batch_label = dict() \n","for i,_ in enumerate(h_batch): #key: hidden states, value: the corresponding batch\n","  # i is the number\n","  batch_label[list(h_batch.values())[i]] = lab[i]\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"JNAgI-PTgeUM"},"source":["with open(PARENT_DIR+'data/dataloaders10perc_batchsize32/mt_train_batch_label.pickle', 'wb') as handle:\n","    pickle.dump(batch_label, handle, protocol=pickle.HIGHEST_PROTOCOL)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"c-_gxpoqjU67"},"source":["# 5. create dataloaders from that new dictionary\n","def create_dataloaders(batch_label):\n","  allloaders = []\n","  for i in range(K_value):\n","    # create groups\n","    a = {key: value for key, value in batch_label.items() if value == i}\n","    print('The',i,'th group has length of ',len(a))\n","    # group to dataloader\n","    datalo = torch.utils.data.DataLoader(list(a),batch_size = 1, shuffle = True)\n","    # store in a list\n","    allloaders.append(datalo)\n","  return allloaders #exp: [mt_val_dl1, mt_val_dl0]\n","\n","dls = create_dataloaders(batch_label)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Sc-P9Z6plUYy"},"source":["# 6. save dataloaders dls\n","\n","with open(PARENT_DIR+'data/dataloaders10perc_batchsize32/mt_train_dataloaders.pickle', 'wb') as handle:\n","    pickle.dump(dls, handle, protocol=pickle.HIGHEST_PROTOCOL)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"tzc2Pm9Tx8fI"},"source":["# Training loops"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"A6gT8moERPw3","executionInfo":{"status":"ok","timestamp":1638838056296,"user_tz":300,"elapsed":3078,"user":{"displayName":"Jie Sun","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15727073160566641365"}},"outputId":"4362dd2d-56c9-4188-d92b-fe409585bc7b"},"source":["import pickle\n","from typing import List, Tuple, Dict\n","\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import pandas as pd\n","\n","import torch\n","from torch import nn, optim\n","from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss\n","from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence, pad_sequence\n","from torch.utils.data import DataLoader, Dataset\n","from torch.distributions.beta import Beta\n","\n","from torchtext.data.metrics import bleu_score\n","\n","import nltk\n","from nltk.tokenize import word_tokenize\n","nltk.download('punkt')\n","tokenizer = nltk.RegexpTokenizer(r\"\\w+\")\n","\n","from tqdm import tqdm\n","\n","from torch.optim import Adam\n","from torch.nn import BCEWithLogitsLoss\n","\n","from EncoderLSTM import EncoderLSTM\n","from DecoderLSTM import DecoderLSTM\n","\n","from train import train\n","\n","from Seq2Seq import Seq2Seq\n","\n","from random import randrange\n","import random"],"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["[nltk_data] Downloading package punkt to /root/nltk_data...\n","[nltk_data]   Unzipping tokenizers/punkt.zip.\n"]}]},{"cell_type":"code","metadata":{"id":"79lIzDcSR04U","executionInfo":{"status":"ok","timestamp":1638838058651,"user_tz":300,"elapsed":2359,"user":{"displayName":"Jie Sun","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15727073160566641365"}}},"source":["with open(PARENT_DIR+'data/dataloaders10perc_batchsize32/mt_train_dataloaders.pickle', 'rb') as handle:\n","    dls = pickle.load(handle)"],"execution_count":9,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"X_2Vbc_lW3Dg","executionInfo":{"status":"ok","timestamp":1638838058883,"user_tz":300,"elapsed":237,"user":{"displayName":"Jie Sun","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15727073160566641365"}},"outputId":"16d9bf4d-fee3-4f5d-a72a-61c93ebd50ab"},"source":["# draw 2 sentences from the same cluster\n","\n","# take from dls[0]\n","b1 = next(iter(dls[1]))\n","b2 = next(iter(dls[1]))\n","\n","# list out keys and values separately\n","key_list = list(mt_train_ds.target_vocab.keys())\n","val_list = list(mt_train_ds.target_vocab.values())\n"," \n","# print key with val 100\n","sent = ''\n","for b in b1[1][0]:\n","  position = val_list.index(b)\n","  word = key_list[position]\n","  sent = sent + ' '+ word\n","print(sent)\n","\n","sent = ''\n","for b in b2[1][0]:\n","  position = val_list.index(b)\n","  word = key_list[position]\n","  sent = sent + ' '+ word\n","print(sent)"],"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":[" <BOS> if you were going to take an airplane trip across the u s you would be flying at these <UNK> <EOS>\n"," <BOS> someday we may find some slight hole in that argument <EOS>\n"]}]},{"cell_type":"code","metadata":{"id":"WBXVCl3UR1NB"},"source":["def train(dataloaders, #1)\n","          similarity,\n","          encoder, \n","          train_ds: Dataset, \n","          train_dl: DataLoader, \n","          val_ds: Dataset, \n","          val_dl: DataLoader, \n","          device, bos_idx, eos_idx, path, overwrite = True, epochs: int = 20, hidden_size = 512, padding_idx = 1, lr = 0.001, weight_decay = 1e-4,\n","          full_loss = False, augmentation_type = None, accum_iter = 32):\n","    \n","    if not os.path.isdir(path):\n","      print('creating directory', path)\n","      os.mkdir(path)\n","    else:\n","      print('directory already exists')\n","      print(path, 'contains', os.listdir(path))\n","      if overwrite:\n","        print('continuing anyways... some files may be overwritten')\n","      else:\n","        print('set overwrite = True to continue')\n","        return None\n","\n","    if not path.endswith('/'):\n","      path = path + '/'\n","\n","    # initialize model, optimizer, and loss functon\n","    model = Seq2Seq(encoder, device, len(train_ds.source_vocab), len(train_ds.target_vocab), \n","                    hidden_size = hidden_size, padding_idx = padding_idx, augmentation_type = augmentation_type).to(device)\n","    optimizer = Adam(params = model.parameters(), lr = lr, weight_decay = weight_decay)\n","    if augmentation_type == 'seqmix_pos_ind' or augmentation_type == 'seqmix_pos_ind_r' or augmentation_type == 'seqmix_ind'  or augmentation_type == 'seqmix_ind_2' or augmentation_type == 'seqmix_pos_ind_r' or augmentation_type == 'seqmix_pos2' or augmentation_type == 'seqmix_ind_pos':\n","        loss_fn_seqmix_ind = CrossEntropyLoss(ignore_index = padding_idx, reduce = False)\n","        # loss_fn_seqmix_ind = label_smoothed_nll_loss(epsilon = 0.1, ignore_index = padding_idx, reduce = False)\n","\n","        loss_fn = CrossEntropyLoss(ignore_index = padding_idx)\n","    else:\n","        loss_fn = CrossEntropyLoss(ignore_index = padding_idx)\n","        \n","    # keep track of losses\n","    losses = {\n","        'train': [], \n","        'val': []\n","    }\n","\n","    val_bleus = []\n","    model_paths = []\n","\n","    # if augmentation_type == 'seqmix' or augmentation_type == 'seqmix_lenmatch' or augmentation_type == 'seqmix_ind'  or augmentation_type == 'seqmix_ind_2' or augmentation_type == 'seqmix_pos' or augmentation_type == 'seqmix_pos_ind' or augmentation_type == 'seqmix_pos_ind_r' or augmentation_type == 'seqmix_pos2' or augmentation_type == 'seqmix_ind_pos':\n","    #   #mix_dl = iter(train_dl) #3)\n","    #     pass #5)\\\n","    mix_dl = None\n","    for e in range(epochs):\n","        running_loss = 0\n","        for j in range(len(dataloaders)): #2)\n","          if similarity and mix_dl == None:\n","              mix_dl = iter(dataloaders[j])\n","          elif (not similarity) and mix_dl == None:\n","              if j != 0:\n","                mix_dl = iter(dataloaders[0])\n","              else:\n","                mix_dl = iter(dataloaders[1])\n","          for batch_idx, batch in enumerate(tqdm(dataloaders[j], leave = False)):            \n","              if augmentation_type == 'seqmix' or augmentation_type == 'seqmix_lenmatch' or augmentation_type == 'seqmix_pos' or augmentation_type == 'seqmix_pos_ind' or augmentation_type == 'seqmix_ind'  or augmentation_type == 'seqmix_ind_2' or augmentation_type == 'seqmix_ind_pos' or augmentation_type == 'seqmix_pos_ind_r' or augmentation_type == 'seqmix_pos2':\n","                # mixer_batch = next(iter(train_dl))\n","                try:\n","                    mix_batch = next(mix_dl)\n","                except:\n","                    if similarity:\n","                      mix_dl = iter(dataloaders[j])\n","                      mix_batch = next(mix_dl)\n","                    else:\n","                      list2 = list(range(j)) + list(range(j, len(dataloaders)))\n","                      g = list2[random.randint(0,len(list2)-1)]\n","                      mix_dl = iter(dataloaders[g])\n","                      mix_batch = next(mix_dl)\n","                  \n","                # mix_batch = next(iter(dls[j]))\n","                \n","                # ### experiment: similar length for seqmix\n","                # if augmentation_type == 'seqmix_lenmatch':\n","                #     param = 0\n","                #     while abs((len(mix_batch[0][0])- len(batch[0][0])) > param) or torch.equal(batch[0][0], mix_batch[0][0]):\n","                #         try:\n","                #             mix_batch = next(mix_dl)\n","                #         except StopIteration:\n","                          \n","                #             mix_dl = iter(train_dl)\n","                #             mix_batch = next(mix_dl)\n","                #             param = param + 1\n","                #             if param > 10:\n","                #                 param += 40\n","              \n","                input_batch = pad_sequence([batch[0][0],mix_batch[0][0]], batch_first=True, padding_value=padding_idx)\n","                target_batch = pad_sequence([batch[1][0],mix_batch[1][0]], batch_first=True, padding_value=padding_idx)\n","                if augmentation_type == 'seqmix' or augmentation_type == 'seqmix_lenmatch':\n","                    pred, targ, lambda_ = model(input_batch[0].unsqueeze(0).to(device), target_batch[0].unsqueeze(0).to(device), seqmix_batch = (input_batch[1].unsqueeze(0).to(device), target_batch[1].unsqueeze(0).to(device)))\n","                    \n","                    l = seqmix_loss(loss_fn, pred, targ, padding_idx, lambda_)\n","                elif augmentation_type == 'seqmix_pos':\n","                    \n","                    pred, targ, lambda_ = model(input_batch[0].unsqueeze(0).to(device), target_batch[0].unsqueeze(0).to(device), seqmix_batch = (input_batch[1].unsqueeze(0).to(device), target_batch[1].unsqueeze(0).to(device)), input_pos = (batch[2].to(device), batch[3].to(device)), mix_pos = (mix_batch[2].to(device), mix_batch[3].to(device)), seqmix_pos = True)\n","\n","                    l = seqmix_loss(loss_fn, pred, targ, padding_idx, lambda_)\n","\n","                elif augmentation_type == 'seqmix_pos_ind_r':\n","                    pred, targ = model(input_batch[0].unsqueeze(0).to(device), target_batch[0].unsqueeze(0).to(device), seqmix_batch = (input_batch[1].unsqueeze(0).to(device), target_batch[1].unsqueeze(0).to(device)), input_pos = (batch[2].to(device), batch[3].to(device)), mix_pos = (mix_batch[2].to(device), mix_batch[3].to(device)), seqmix_pos_ind = True)\n","                    \n","                    l = loss_fn(pred, targ)\n","\n","                elif augmentation_type == 'seqmix_pos_ind' or augmentation_type == 'seqmix_pos2':\n","                    pred, targ, lambda_targ = model(input_batch[0].unsqueeze(0).to(device), target_batch[0].unsqueeze(0).to(device), seqmix_batch = (input_batch[1].unsqueeze(0).to(device), target_batch[1].unsqueeze(0).to(device)), input_pos = (batch[2].to(device), batch[3].to(device)), mix_pos = (mix_batch[2].to(device), mix_batch[3].to(device)), seqmix_pos_ind = True)\n","                    \n","                    l = seqmix_ind_loss(pred, targ, target_batch[0], target_batch[1], padding_idx, lambda_targ)\n","\n","                elif augmentation_type == 'seqmix_ind' or augmentation_type == 'seqmix_ind_2':\n","                    pred, targ, lambda_targ = model(input_batch[0].unsqueeze(0).to(device), target_batch[0].unsqueeze(0).to(device), seqmix_batch = (input_batch[1].unsqueeze(0).to(device), target_batch[1].unsqueeze(0).to(device)), seqmix_ind = True)\n","                    \n","                    l = seqmix_ind_loss(loss_fn, pred, targ, target_batch[0], target_batch[1], padding_idx, lambda_targ)\n","                    \n","                elif augmentation_type == 'seqmix_ind_pos':\n","                    pred, targ, lambda_pos = model(input_batch[0].unsqueeze(0).to(device), target_batch[0].unsqueeze(0).to(device), seqmix_batch = (input_batch[1].unsqueeze(0).to(device), target_batch[1].unsqueeze(0).to(device)), input_pos = (batch[2].to(device), batch[3].to(device)), mix_pos = (mix_batch[2].to(device), mix_batch[3].to(device)))\n","                  #   loss_fn, pred, targ, targ_pos, padding_idx, lambda_targ\n","                    l = seqmix_ind_pos_loss(loss_fn_seqmix_ind, pred, targ, batch[3].to(device), padding_idx, lambda_pos)\n","\n","              else:\n","                # predict + compute loss\n","                pred, targ = model(input_seqs = batch[0].to(device), output_seqs = batch[1].to(device))\n","                l = loss_fn(pred, targ)\n","              \n","              running_loss += l.item()\n","              l = l/accum_iter\n","\n","              # update\n","              l.backward()\n","\n","              if ((batch_idx + 1) % accum_iter == 0) or (batch_idx + 1 == len(train_dl)):\n","                optimizer.step()\n","                optimizer.zero_grad()\n","          # reset\n","          mix_dl = None\n","          \n","# after running all 5 sub dataloaders\n","        if full_loss:\n","          # record losses computed on full training and validation datasets\n","          full_training_loss = compute_test_loss(model, train_dl, loss_fn)\n","        else:\n","          full_training_loss = running_loss/len(train_dl)\n","\n","        candidate_text, reference_text = translate_corpus(\n","          train_ds.target_vocab, val_dl, model, \n","          eos_idx = eos_idx, bos_idx = bos_idx\n","        )\n","        bleu = bleu_score(\n","          candidate_text, reference_text\n","        )\n","        val_bleus.append(bleu)\n","\n","        full_val_loss = compute_test_loss(model, val_dl, loss_fn)\n","        losses['train'].append(full_training_loss)\n","        losses['val'].append(full_val_loss)\n","\n","        model_path = path + 'epoch' + str(e)\n","        torch.save(model, model_path)\n","        model_paths.append(model_path)\n","\n","        s = 'epoch ' + str(e) +': training loss = ' + \\\n","            str(round(full_training_loss, 5)) + ' - validation loss = ' + str(round(full_val_loss, 5)) + \\\n","            ' - validation bleu = ' + str(round(bleu, 5))\n","            \n","        print(s)\n","\n","    best_bleu = max(val_bleus)\n","    which = val_bleus.index(best_bleu)\n","    best_model_path = model_paths[which]\n","    print(\"best model path:\", best_model_path)\n","    best_model = torch.load(best_model_path)\n","\n","    return (best_model, losses, val_bleus, which)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"QHBKkDPsU_6U"},"source":["@torch.no_grad()\n","def compute_test_loss(model, val_dl, loss_fn):\n","    all_preds, all_targs = [], []\n","    for i, (input_seqs, output_seqs, input_pos, output_pos) in enumerate(val_dl):\n","        preds, targs = model(input_seqs.to(device), output_seqs.to(device), generate = True)\n","        all_preds.append(preds)\n","        all_targs.append(targs)\n","    preds = torch.cat(all_preds, dim=0)\n","    targs = torch.cat(all_targs, dim=0)\n","    return loss_fn(preds, targs).item()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"g1u0GPWGWYfX"},"source":["def seqmix_loss(loss_fn, pred, targ, padding_idx, lambda_):\n","    l1 = loss_fn(pred, targ[0])\n","    l2 = loss_fn(pred, targ[1])\n","    loss = l1 * lambda_.view(-1, 1) + l2 * (1-lambda_).view(-1, 1)\n","    \n","    return loss"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"o6d5SAG2Ursf"},"source":["MAX_STEPS = 40\n","def translate_corpus(\n","    vocab: Dict[str, int], \n","    test_dl: DataLoader, \n","    model: nn.Module,\n","    eos_idx,\n","    bos_idx,\n","    max_steps = MAX_STEPS\n",") -> Tuple[List[List[str]], List[List[List[str]]]]:\n","    \n","    index_to_word = {v: k for k, v in vocab.items()}\n","    candidate_text = []\n","    reference_text = []\n","\n","    for source, target, source_pos, target_pos in test_dl:\n","        for i in range(len(source)):  # for seq in batch\n","            source = source.to(device)\n","\n","            cand = model.generate(source[i].view(1, -1), max_steps = max_steps, eos_idx = eos_idx, bos_idx = bos_idx)\n","            cand = [index_to_word[int(cand[j])] for j in range(len(cand))]\n","            ref = [index_to_word[int(target[i][j])] for j in range(len(target[i])) if int(target[i][j]) != 0]\n","            candidate_text.append(cand)\n","            reference_text.append([ref])\n","        \n","    return candidate_text, reference_text"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_Wb9YZESSrAI","executionInfo":{"status":"ok","timestamp":1638802061542,"user_tz":300,"elapsed":7,"user":{"displayName":"Jie Sun","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15727073160566641365"}},"outputId":"178a1730-2bbf-48a5-f41e-a4433464ff15"},"source":["import torch\n","\n","print(torch.cuda.is_available())\n","print(torch.cuda.get_device_name(0))\n","\n","device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","print(device)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["True\n","Tesla P100-PCIE-16GB\n","cuda\n"]}]},{"cell_type":"code","metadata":{"id":"ALlYtzt8SkGm"},"source":["epochs = 20\n","hidden_size = 512\n","padding_idx = 1\n","n_extra_tokens = 4\n","\n","# Some useful helper functions, which we will need for later...\n","UNK_IDX = 0 \n","PADDING_IDX = 1 \n","BOS_IDX = 2\n","EOS_IDX = 3"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"qnzmPbV2bxFl"},"source":["## Case 1: dissimilar"]},{"cell_type":"code","metadata":{"id":"Akfi1EBTR5bT","colab":{"base_uri":"https://localhost:8080/","height":714},"executionInfo":{"status":"error","timestamp":1638802208585,"user_tz":300,"elapsed":147047,"user":{"displayName":"Jie Sun","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15727073160566641365"}},"outputId":"b5df690a-f54b-4347-aa12-6a82b498511a"},"source":["#### here is pasted, the most basic version \n","import os\n","encoder = EncoderLSTM(\n","    input_size = len(mt_train_ds.source_vocab), \n","    hidden_size = hidden_size, \n","    padding_idx = padding_idx,\n","    device = device,\n","    augmentation_type = 'seqmix',\n","    gamma = 0.10\n",")\n","\n","# Case 1: dissimilar\n","model, losses, bleus, which = train(dls,\n","    False, ## similarity\n","    encoder,\n","    mt_train_ds, \n","    mt_train_dl, \n","    mt_val_ds, mt_val_dl, \n","    device = device,\n","    augmentation_type = 'seqmix',\n","    epochs = 20, hidden_size = hidden_size,\n","    lr = 0.001, weight_decay = 1*10**(-4),\n","    padding_idx = PADDING_IDX,\n","    eos_idx = EOS_IDX, bos_idx = BOS_IDX,\n","    path = PARENT_DIR + 'seq2seq_modelk10dis/'\n",")\n","\n","pd.DataFrame(losses).plot(xlabel='epoch #', ylabel='loss', \n","                              title='Train and Validation Loss')\n","\n","# SAVE THE BEST\n","torch.save(model, PARENT_DIR + 'k10disbest')\n","print('Best model saved')"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["creating directory /content/gdrive/MyDrive/CS287_Research_Project/Jennas_Code/seq2seq_modelk10dis/\n"]},{"output_type":"stream","name":"stderr","text":[""]},{"output_type":"stream","name":"stdout","text":["epoch 0: training loss = 5.07059 - validation loss = 5.2702 - validation bleu = 0.0\n","best model path: /content/gdrive/MyDrive/CS287_Research_Project/Jennas_Code/seq2seq_modelk10dis/epoch0\n"]},{"output_type":"error","ename":"FileNotFoundError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)","\u001b[0;32m<ipython-input-16-42d9c0bab8e6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;31m# SAVE THE BEST\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPARENT_DIR\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'seqmix_similarity/k10disbest'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Best model saved'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/serialization.py\u001b[0m in \u001b[0;36msave\u001b[0;34m(obj, f, pickle_module, pickle_protocol, _use_new_zipfile_serialization)\u001b[0m\n\u001b[1;32m    374\u001b[0m     \u001b[0m_check_dill_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpickle_module\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    375\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 376\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0m_open_file_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'wb'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mopened_file\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    377\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0m_use_new_zipfile_serialization\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    378\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0m_open_zipfile_writer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopened_file\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"UYysHMQ-lpgk","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1638764716314,"user_tz":300,"elapsed":3034,"user":{"displayName":"Jie Sun","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15727073160566641365"}},"outputId":"955c3f08-7b43-48c7-9905-d1cd261c4667"},"source":["candidate_text, reference_text = translate_corpus(mt_train_ds.target_vocab, mt_test_dl, model, eos_idx = EOS_IDX, bos_idx = BOS_IDX)\n","bleu = bleu_score(candidate_text, reference_text)\n","bleu"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.043570056557655334"]},"metadata":{},"execution_count":25}]},{"cell_type":"code","metadata":{"id":"D4TjEMPKlrlu","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1638764719798,"user_tz":300,"elapsed":3487,"user":{"displayName":"Jie Sun","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15727073160566641365"}},"outputId":"dfebaf37-bec0-43d0-88ae-b4143a635662"},"source":["candidate_text, reference_text = translate_corpus(mt_train_ds.target_vocab, mt_test_dl, model, eos_idx = EOS_IDX, bos_idx = BOS_IDX)\n","bleu = bleu_score(candidate_text, reference_text, max_n = 1, weights=[1])\n","bleu"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.31839999556541443"]},"metadata":{},"execution_count":26}]},{"cell_type":"markdown","metadata":{"id":"i7dvDhnNb08y"},"source":["## Case 2: similar"]},{"cell_type":"code","metadata":{"id":"k0ImeVG7hpgU","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1638768598901,"user_tz":300,"elapsed":3879106,"user":{"displayName":"Jie Sun","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15727073160566641365"}},"outputId":"a2cebf05-7200-46ab-e968-555dfc72425a"},"source":["#### here is pasted, the most basic version \n","import os\n","encoder = EncoderLSTM(\n","    input_size = len(mt_train_ds.source_vocab), \n","    hidden_size = hidden_size, \n","    padding_idx = padding_idx,\n","    device = device,\n","    augmentation_type = 'seqmix',\n","    gamma = 0.10\n",")\n","\n","# Case 2: similar\n","model, losses, bleus, which = train(dls,\n","    True, ## similarity\n","    encoder,\n","    mt_train_ds, \n","    mt_train_dl, \n","    mt_val_ds, mt_val_dl, \n","    device = device,\n","    augmentation_type = 'seqmix',\n","    epochs = 20, hidden_size = hidden_size,\n","    lr = 0.001, weight_decay = 1*10**(-4),\n","    padding_idx = PADDING_IDX,\n","    eos_idx = EOS_IDX, bos_idx = BOS_IDX,\n","    path = PARENT_DIR + 'seq2seq_modelk10sim/'\n",")\n","\n","pd.DataFrame(losses).plot(xlabel='epoch #', ylabel='loss', \n","                              title='Train and Validation Loss')\n","\n","# SAVE THE BEST\n","torch.save(model, PARENT_DIR + 'k10simbest')\n","print('Best model saved')"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["creating directory seq2seq_modelk10sim\n"]},{"output_type":"stream","name":"stderr","text":[""]},{"output_type":"stream","name":"stdout","text":["epoch 0: training loss = 5.01484 - validation loss = 5.46755 - validation bleu = 0.0\n"]},{"output_type":"stream","name":"stderr","text":[""]},{"output_type":"stream","name":"stdout","text":["epoch 1: training loss = 4.5514 - validation loss = 5.06747 - validation bleu = 0.00632\n"]},{"output_type":"stream","name":"stderr","text":[""]},{"output_type":"stream","name":"stdout","text":["epoch 2: training loss = 4.3273 - validation loss = 4.74957 - validation bleu = 0.00933\n"]},{"output_type":"stream","name":"stderr","text":[""]},{"output_type":"stream","name":"stdout","text":["epoch 3: training loss = 4.1715 - validation loss = 4.56136 - validation bleu = 0.02487\n"]},{"output_type":"stream","name":"stderr","text":[""]},{"output_type":"stream","name":"stdout","text":["epoch 4: training loss = 4.04732 - validation loss = 4.46231 - validation bleu = 0.03054\n"]},{"output_type":"stream","name":"stderr","text":[""]},{"output_type":"stream","name":"stdout","text":["epoch 5: training loss = 3.94521 - validation loss = 4.40557 - validation bleu = 0.02801\n"]},{"output_type":"stream","name":"stderr","text":[""]},{"output_type":"stream","name":"stdout","text":["epoch 6: training loss = 3.85934 - validation loss = 4.37264 - validation bleu = 0.03106\n"]},{"output_type":"stream","name":"stderr","text":[""]},{"output_type":"stream","name":"stdout","text":["epoch 7: training loss = 3.7825 - validation loss = 4.33081 - validation bleu = 0.03113\n"]},{"output_type":"stream","name":"stderr","text":[""]},{"output_type":"stream","name":"stdout","text":["epoch 8: training loss = 3.71532 - validation loss = 4.29932 - validation bleu = 0.03714\n"]},{"output_type":"stream","name":"stderr","text":[""]},{"output_type":"stream","name":"stdout","text":["epoch 9: training loss = 3.65339 - validation loss = 4.28171 - validation bleu = 0.03578\n"]},{"output_type":"stream","name":"stderr","text":[""]},{"output_type":"stream","name":"stdout","text":["epoch 10: training loss = 3.59725 - validation loss = 4.24266 - validation bleu = 0.03484\n"]},{"output_type":"stream","name":"stderr","text":[""]},{"output_type":"stream","name":"stdout","text":["epoch 11: training loss = 3.54652 - validation loss = 4.22747 - validation bleu = 0.03687\n"]},{"output_type":"stream","name":"stderr","text":[""]},{"output_type":"stream","name":"stdout","text":["epoch 12: training loss = 3.50027 - validation loss = 4.22727 - validation bleu = 0.03514\n"]},{"output_type":"stream","name":"stderr","text":[""]},{"output_type":"stream","name":"stdout","text":["epoch 13: training loss = 3.45784 - validation loss = 4.22072 - validation bleu = 0.03481\n"]},{"output_type":"stream","name":"stderr","text":[""]},{"output_type":"stream","name":"stdout","text":["epoch 14: training loss = 3.4194 - validation loss = 4.22278 - validation bleu = 0.04244\n"]},{"output_type":"stream","name":"stderr","text":[""]},{"output_type":"stream","name":"stdout","text":["epoch 15: training loss = 3.38392 - validation loss = 4.21708 - validation bleu = 0.03611\n"]},{"output_type":"stream","name":"stderr","text":[""]},{"output_type":"stream","name":"stdout","text":["epoch 16: training loss = 3.35187 - validation loss = 4.1983 - validation bleu = 0.04949\n"]},{"output_type":"stream","name":"stderr","text":[""]},{"output_type":"stream","name":"stdout","text":["epoch 17: training loss = 3.31935 - validation loss = 4.19876 - validation bleu = 0.04903\n"]},{"output_type":"stream","name":"stderr","text":[""]},{"output_type":"stream","name":"stdout","text":["epoch 18: training loss = 3.28691 - validation loss = 4.20079 - validation bleu = 0.0417\n"]},{"output_type":"stream","name":"stderr","text":[""]},{"output_type":"stream","name":"stdout","text":["epoch 19: training loss = 3.25571 - validation loss = 4.19825 - validation bleu = 0.04047\n","best model path: seq2seq_modelk10sim/epoch16\n"]},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.axes._subplots.AxesSubplot at 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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Zh9C6jrbquqY","executionInfo":{"status":"ok","timestamp":1638768602324,"user_tz":300,"elapsed":3433,"user":{"displayName":"Jie Sun","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15727073160566641365"}},"outputId":"c97e80df-a7d5-42c2-a312-d8fdebd7fed3"},"source":["candidate_text, reference_text = translate_corpus(mt_train_ds.target_vocab, mt_test_dl, model, eos_idx = EOS_IDX, bos_idx = BOS_IDX)\n","bleu = bleu_score(candidate_text, reference_text)\n","bleu"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.04066626727581024"]},"metadata":{},"execution_count":28}]},{"cell_type":"code","metadata":{"id":"hpf80MGbe3z-","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1638768606473,"user_tz":300,"elapsed":4153,"user":{"displayName":"Jie Sun","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"15727073160566641365"}},"outputId":"1c1fc68d-54b6-46af-8920-68dcf5e66f93"},"source":["candidate_text, reference_text = translate_corpus(mt_train_ds.target_vocab, mt_test_dl, model, eos_idx = EOS_IDX, bos_idx = BOS_IDX)\n","bleu = bleu_score(candidate_text, reference_text, max_n = 1, weights=[1])\n","bleu"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.30762138962745667"]},"metadata":{},"execution_count":29}]},{"cell_type":"markdown","metadata":{"id":"7U8P1Z-flO9f"},"source":["# 2nd run"]},{"cell_type":"markdown","metadata":{"id":"V-TxmAKHlRTQ"},"source":["## Case 1: Dissimilar"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"O8RkZqdblQ2Q","outputId":"3dd8489d-eb23-4003-8041-25e769df1e87"},"source":["#### here is pasted, the most basic version \n","import os\n","encoder = EncoderLSTM(\n","    input_size = len(mt_train_ds.source_vocab), \n","    hidden_size = hidden_size, \n","    padding_idx = padding_idx,\n","    device = device,\n","    augmentation_type = 'seqmix',\n","    gamma = 0.10\n",")\n","\n","# Case 1: dissimilar\n","model, losses, bleus, which = train(dls,\n","    False, ## similarity\n","    encoder,\n","    mt_train_ds, \n","    mt_train_dl, \n","    mt_val_ds, mt_val_dl, \n","    device = device,\n","    augmentation_type = 'seqmix',\n","    epochs = 21, hidden_size = hidden_size,\n","    lr = 0.0005, weight_decay = 1*10**(-6),\n","    padding_idx = PADDING_IDX,\n","    eos_idx = EOS_IDX, bos_idx = BOS_IDX,\n","    path = 'seq2seq_models3'\n",")\n","\n","pd.DataFrame(losses).plot(xlabel='epoch #', ylabel='loss', \n","                              title='Train and Validation Loss')"],"execution_count":null,"outputs":[{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["creating directory seq2seq_models3\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":[""]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["epoch 0: training loss = 5.18916 - validation loss = 5.30017 - validation bleu = 0.00811\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":[""]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["epoch 1: training loss = 4.73121 - validation loss = 4.9585 - validation bleu = 0.00428\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":[""]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["epoch 2: training loss = 4.4745 - validation loss = 4.71736 - validation bleu = 0.00764\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":[""]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["epoch 3: training loss = 4.25901 - validation loss = 4.57646 - validation bleu = 0.01531\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":[""]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["epoch 4: training loss = 4.08407 - validation loss = 4.46314 - validation bleu = 0.02842\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":[""]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["epoch 5: training loss = 3.92941 - validation loss = 4.40071 - validation bleu = 0.03253\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":[""]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["epoch 6: training loss = 3.77301 - validation loss = 4.3611 - validation bleu = 0.03682\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":[""]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["epoch 7: training loss = 3.6319 - validation loss = 4.3386 - validation bleu = 0.04065\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":[""]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["epoch 8: training loss = 3.50377 - validation loss = 4.33701 - validation bleu = 0.03837\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":[""]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["epoch 9: training loss = 3.3585 - validation loss = 4.32572 - validation bleu = 0.02542\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":[""]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["epoch 10: training loss = 3.23396 - validation loss = 4.31136 - validation bleu = 0.03604\n"]},{"metadata":{"tags":null},"name":"stderr","output_type":"stream","text":[""]},{"metadata":{"tags":null},"name":"stdout","output_type":"stream","text":["epoch 11: training loss = 3.11036 - validation loss = 4.31518 - validation bleu = 0.03937\n"]},{"output_type":"stream","name":"stderr","text":[" 72%|███████▏  | 1704/2356 [00:16<00:05, 109.27it/s]"]}]},{"cell_type":"code","metadata":{"id":"5PKYKYsOlVfe"},"source":["candidate_text, reference_text = translate_corpus(mt_train_ds.target_vocab, mt_test_dl, model, eos_idx = EOS_IDX, bos_idx = BOS_IDX)\n","bleu = bleu_score(candidate_text, reference_text)\n","bleu"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"3B9Xks0alX67"},"source":["candidate_text, reference_text = translate_corpus(mt_train_ds.target_vocab, mt_test_dl, model, eos_idx = EOS_IDX, bos_idx = BOS_IDX)\n","bleu = bleu_score(candidate_text, reference_text, max_n = 1, weights=[1])\n","bleu"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"idi-ZAOclZ4o"},"source":["## Case 2: similar"]},{"cell_type":"code","metadata":{"id":"jdBuOLzmlZVc"},"source":["#### here is pasted, the most basic version \n","import os\n","encoder = EncoderLSTM(\n","    input_size = len(mt_train_ds.source_vocab), \n","    hidden_size = hidden_size, \n","    padding_idx = padding_idx,\n","    device = device,\n","    augmentation_type = 'seqmix',\n","    gamma = 0.10\n",")\n","\n","# Case 2: similar\n","model, losses, bleus, which = train(dls,\n","    True, ## similarity\n","    encoder,\n","    mt_train_ds, \n","    mt_train_dl, \n","    mt_val_ds, mt_val_dl, \n","    device = device,\n","    augmentation_type = 'seqmix',\n","    epochs = 21, hidden_size = hidden_size,\n","    lr = 0.0005, weight_decay = 1*10**(-6),\n","    padding_idx = PADDING_IDX,\n","    eos_idx = EOS_IDX, bos_idx = BOS_IDX,\n","    path = 'seq2seq_models3'\n",")\n","\n","pd.DataFrame(losses).plot(xlabel='epoch #', ylabel='loss', \n","                              title='Train and Validation Loss')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"RJXUv_6UldcN"},"source":["candidate_text, reference_text = translate_corpus(mt_train_ds.target_vocab, mt_test_dl, model, eos_idx = EOS_IDX, bos_idx = BOS_IDX)\n","bleu = bleu_score(candidate_text, reference_text)\n","bleu"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"iyKncrhLlfR-"},"source":["candidate_text, reference_text = translate_corpus(mt_train_ds.target_vocab, mt_test_dl, model, eos_idx = EOS_IDX, bos_idx = BOS_IDX)\n","bleu = bleu_score(candidate_text, reference_text, max_n = 1, weights=[1])\n","bleu"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"xkHka-Rzlgte"},"source":[""],"execution_count":null,"outputs":[]}]}