Example.ipynb

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     "text": [
      "Cloning into 'generative-aste'...\r\n",
      "remote: Enumerating objects: 877, done.\u001b[K\r\n",
      "remote: Counting objects: 100% (233/233), done.\u001b[K\r\n",
      "remote: Compressing objects: 100% (153/153), done.\u001b[K\r\n",
      "remote: Total 877 (delta 128), reused 172 (delta 79), pack-reused 644\u001b[K\r\n",
      "Receiving objects: 100% (877/877), 8.47 MiB | 13.76 MiB/s, done.\r\n",
      "Resolving deltas: 100% (532/532), done.\r\n"
     ]
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   ],
   "source": [
    "# Git clone the projects..."
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     "text": [
      "/kaggle/working/generative-aste\n",
      "Collecting transformers==4.12.5\r\n",
      "  Downloading transformers-4.12.5-py3-none-any.whl (3.1 MB)\r\n",
      "     |████████████████████████████████| 3.1 MB 781 kB/s            \r\n",
      "\u001b[?25hCollecting scikit-learn==0.23.2\r\n",
      "  Downloading scikit_learn-0.23.2-cp37-cp37m-manylinux1_x86_64.whl (6.8 MB)\r\n",
      "     |████████████████████████████████| 6.8 MB 58.6 MB/s            \r\n",
      "\u001b[?25hCollecting pandas==1.3.4\r\n",
      "  Downloading pandas-1.3.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (11.3 MB)\r\n",
      "     |████████████████████████████████| 11.3 MB 48.7 MB/s            \r\n",
      "\u001b[?25hCollecting editdistance==0.6.0\r\n",
      "  Downloading editdistance-0.6.0-cp37-cp37m-manylinux2010_x86_64.whl (285 kB)\r\n",
      "     |████████████████████████████████| 285 kB 55.7 MB/s            \r\n",
      "\u001b[?25hRequirement already satisfied: torch==1.9.1 in /opt/conda/lib/python3.7/site-packages (from -r requirements.txt (line 5)) (1.9.1)\r\n",
      "Requirement already satisfied: torchmetrics==0.6.2 in /opt/conda/lib/python3.7/site-packages (from -r requirements.txt (line 6)) (0.6.2)\r\n",
      "Collecting numpy==1.19.5\r\n",
      "  Downloading numpy-1.19.5-cp37-cp37m-manylinux2010_x86_64.whl (14.8 MB)\r\n",
      "     |████████████████████████████████| 14.8 MB 47.4 MB/s            \r\n",
      "\u001b[?25hRequirement already satisfied: tqdm==4.62.3 in /opt/conda/lib/python3.7/site-packages (from -r requirements.txt (line 8)) (4.62.3)\r\n",
      "Requirement already satisfied: sentencepiece==0.1.96 in /opt/conda/lib/python3.7/site-packages (from -r requirements.txt (line 9)) (0.1.96)\r\n",
      "Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /opt/conda/lib/python3.7/site-packages (from transformers==4.12.5->-r requirements.txt (line 1)) (0.10.3)\r\n",
      "Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.7/site-packages (from transformers==4.12.5->-r requirements.txt (line 1)) (6.0)\r\n",
      "Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /opt/conda/lib/python3.7/site-packages (from transformers==4.12.5->-r requirements.txt (line 1)) (0.2.1)\r\n",
      "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.7/site-packages (from transformers==4.12.5->-r requirements.txt (line 1)) (2021.11.10)\r\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.7/site-packages (from transformers==4.12.5->-r requirements.txt (line 1)) (3.4.2)\r\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.7/site-packages (from transformers==4.12.5->-r requirements.txt (line 1)) (21.3)\r\n",
      "Requirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (from transformers==4.12.5->-r requirements.txt (line 1)) (2.26.0)\r\n",
      "Requirement already satisfied: sacremoses in /opt/conda/lib/python3.7/site-packages (from transformers==4.12.5->-r requirements.txt (line 1)) (0.0.47)\r\n",
      "Requirement already satisfied: importlib-metadata in /opt/conda/lib/python3.7/site-packages (from transformers==4.12.5->-r requirements.txt (line 1)) (4.11.2)\r\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from scikit-learn==0.23.2->-r requirements.txt (line 2)) (3.0.0)\r\n",
      "Requirement already satisfied: scipy>=0.19.1 in /opt/conda/lib/python3.7/site-packages (from scikit-learn==0.23.2->-r requirements.txt (line 2)) (1.7.3)\r\n",
      "Requirement already satisfied: joblib>=0.11 in /opt/conda/lib/python3.7/site-packages (from scikit-learn==0.23.2->-r requirements.txt (line 2)) (1.1.0)\r\n",
      "Requirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.7/site-packages (from pandas==1.3.4->-r requirements.txt (line 3)) (2021.3)\r\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.7/site-packages (from pandas==1.3.4->-r requirements.txt (line 3)) (2.8.2)\r\n",
      "Requirement already satisfied: typing-extensions in /opt/conda/lib/python3.7/site-packages (from torch==1.9.1->-r requirements.txt (line 5)) (4.1.1)\r\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.7/site-packages (from packaging>=20.0->transformers==4.12.5->-r requirements.txt (line 1)) (3.0.6)\r\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas==1.3.4->-r requirements.txt (line 3)) (1.16.0)\r\n",
      "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata->transformers==4.12.5->-r requirements.txt (line 1)) (3.6.0)\r\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests->transformers==4.12.5->-r requirements.txt (line 1)) (3.1)\r\n",
      "Requirement already satisfied: charset-normalizer~=2.0.0 in /opt/conda/lib/python3.7/site-packages (from requests->transformers==4.12.5->-r requirements.txt (line 1)) (2.0.9)\r\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests->transformers==4.12.5->-r requirements.txt (line 1)) (1.26.7)\r\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests->transformers==4.12.5->-r requirements.txt (line 1)) (2021.10.8)\r\n",
      "Requirement already satisfied: click in /opt/conda/lib/python3.7/site-packages (from sacremoses->transformers==4.12.5->-r requirements.txt (line 1)) (8.0.3)\r\n",
      "Installing collected packages: numpy, transformers, scikit-learn, pandas, editdistance\r\n",
      "  Attempting uninstall: numpy\r\n",
      "    Found existing installation: numpy 1.20.3\r\n",
      "    Uninstalling numpy-1.20.3:\r\n",
      "      Successfully uninstalled numpy-1.20.3\r\n",
      "  Attempting uninstall: transformers\r\n",
      "    Found existing installation: transformers 4.15.0\r\n",
      "    Uninstalling transformers-4.15.0:\r\n",
      "      Successfully uninstalled transformers-4.15.0\r\n",
      "  Attempting uninstall: scikit-learn\r\n",
      "    Found existing installation: scikit-learn 1.0.1\r\n",
      "    Uninstalling scikit-learn-1.0.1:\r\n",
      "      Successfully uninstalled scikit-learn-1.0.1\r\n",
      "  Attempting uninstall: pandas\r\n",
      "    Found existing installation: pandas 1.3.5\r\n",
      "    Uninstalling pandas-1.3.5:\r\n",
      "      Successfully uninstalled pandas-1.3.5\r\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
      "tensorflow-io 0.21.0 requires tensorflow-io-gcs-filesystem==0.21.0, which is not installed.\r\n",
      "explainable-ai-sdk 1.3.2 requires xai-image-widget, which is not installed.\r\n",
      "dask-cudf 21.10.1 requires cupy-cuda114, which is not installed.\r\n",
      "beatrix-jupyterlab 3.1.6 requires google-cloud-bigquery-storage, which is not installed.\r\n",
      "yellowbrick 1.4 requires scikit-learn>=1.0.0, but you have scikit-learn 0.23.2 which is incompatible.\r\n",
      "thinc 8.0.13 requires typing-extensions<4.0.0.0,>=3.7.4.1; python_version < \"3.8\", but you have typing-extensions 4.1.1 which is incompatible.\r\n",
      "tfx-bsl 1.5.0 requires absl-py<0.13,>=0.9, but you have absl-py 0.15.0 which is incompatible.\r\n",
      "tfx-bsl 1.5.0 requires pyarrow<6,>=1, but you have pyarrow 6.0.1 which is incompatible.\r\n",
      "tfx-bsl 1.5.0 requires tensorflow!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,<3,>=1.15.2, but you have tensorflow 2.6.2 which is incompatible.\r\n",
      "tensorflow 2.6.2 requires six~=1.15.0, but you have six 1.16.0 which is incompatible.\r\n",
      "tensorflow 2.6.2 requires typing-extensions~=3.7.4, but you have typing-extensions 4.1.1 which is incompatible.\r\n",
      "tensorflow 2.6.2 requires wrapt~=1.12.1, but you have wrapt 1.13.3 which is incompatible.\r\n",
      "tensorflow-transform 1.5.0 requires absl-py<0.13,>=0.9, but you have absl-py 0.15.0 which is incompatible.\r\n",
      "tensorflow-transform 1.5.0 requires pyarrow<6,>=1, but you have pyarrow 6.0.1 which is incompatible.\r\n",
      "tensorflow-transform 1.5.0 requires tensorflow!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.*,!=2.4.*,!=2.5.*,!=2.6.*,<2.8,>=1.15.2, but you have tensorflow 2.6.2 which is incompatible.\r\n",
      "tensorflow-serving-api 2.7.0 requires tensorflow<3,>=2.7.0, but you have tensorflow 2.6.2 which is incompatible.\r\n",
      "spacy 3.2.2 requires typing-extensions<4.0.0.0,>=3.7.4; python_version < \"3.8\", but you have typing-extensions 4.1.1 which is incompatible.\r\n",
      "pdpbox 0.2.1 requires matplotlib==3.1.1, but you have matplotlib 3.5.1 which is incompatible.\r\n",
      "imbalanced-learn 0.9.0 requires scikit-learn>=1.0.1, but you have scikit-learn 0.23.2 which is incompatible.\r\n",
      "hypertools 0.8.0 requires scikit-learn>=0.24, but you have scikit-learn 0.23.2 which is incompatible.\r\n",
      "featuretools 1.6.0 requires numpy>=1.21.0, but you have numpy 1.19.5 which is incompatible.\r\n",
      "dask-cudf 21.10.1 requires dask==2021.09.1, but you have dask 2022.2.0 which is incompatible.\r\n",
      "dask-cudf 21.10.1 requires distributed==2021.09.1, but you have distributed 2022.2.0 which is incompatible.\r\n",
      "arviz 0.11.4 requires typing-extensions<4,>=3.7.4.3, but you have typing-extensions 4.1.1 which is incompatible.\r\n",
      "apache-beam 2.34.0 requires dill<0.3.2,>=0.3.1.1, but you have dill 0.3.4 which is incompatible.\r\n",
      "apache-beam 2.34.0 requires httplib2<0.20.0,>=0.8, but you have httplib2 0.20.2 which is incompatible.\r\n",
      "apache-beam 2.34.0 requires pyarrow<6.0.0,>=0.15.1, but you have pyarrow 6.0.1 which is incompatible.\r\n",
      "apache-beam 2.34.0 requires typing-extensions<4,>=3.7.0, but you have typing-extensions 4.1.1 which is incompatible.\u001b[0m\r\n",
      "Successfully installed editdistance-0.6.0 numpy-1.19.5 pandas-1.3.4 scikit-learn-0.23.2 transformers-4.12.5\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n"
     ]
    }
   ],
   "source": [
    "%cd generative-aste/\n",
    "!pip install -r requirements.txt "
   ]
  },
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "General-Aspect.ipynb  archive  dataset\tnotebook\t  resources\r\n",
      "LICENSE\t\t      args.py  demo.py\tparse.py\t  scripts\r\n",
      "Makefile\t      bin      docs\tposttraining.py   setup.sh\r\n",
      "README.md\t      data     main.py\trequirements.txt  src\r\n"
     ]
    }
   ],
   "source": [
    "!ls "
   ]
  },
  {
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   "id": "58b1b785",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "!rm -r data/processed/unfilter/\n",
    "!mkdir data/processed/unfilter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cd85ed24",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dev.txt  test.txt  train.txt\r\n"
     ]
    }
   ],
   "source": [
    "!ls ../../input/hotelastev2/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b2cf5685",
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "!cp -f ../../input/hotelastev2/dev.txt data/processed/unfilter\n",
    "!cp -f ../../input/hotelastev2/train.txt data/processed/unfilter\n",
    "!cp -f ../../input/hotelastev2/test.txt data/processed/unfilter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7a41f6ae",
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dev.txt  test.txt  train.txt\r\n"
     ]
    }
   ],
   "source": [
    "!ls data/processed/unfilter/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4b39b8b8",
   "metadata": {
    "execution": {
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Namespace(config='resources/exp-v2/exp-m0.yaml', do_test=True, do_train=True, prefix='exp-m0-m2')\r\n",
      "Tokenizer type: Wikidepia/IndoT5-base\r\n",
      "Downloading: 100%|███████████████████████████| 759k/759k [00:00<00:00, 1.22MB/s]\r\n",
      "Downloading: 100%|██████████████████████████████| 696/696 [00:00<00:00, 572kB/s]\r\n",
      "unfilter\r\n",
      "\r\n",
      "Dataset: Training\r\n",
      "--------------------------------------------------------\r\n",
      "Details                          Value                     \r\n",
      "========================================================\r\n",
      "Total Instance                   3000        \r\n",
      "Total Triplet Instance           6251        \r\n",
      "Triplet avg (per review)         2.083666666666667\r\n",
      "Triplet min (per review)         0           \r\n",
      "Triplet max (per review)         14          \r\n",
      "Sequence length max              96          \r\n",
      "Sequence length min              2           \r\n",
      "Sequence length avg              15.911333333333333\r\n",
      "Unique sequence length max       73          \r\n",
      "Unique sequence length min       2           \r\n",
      "Unique sequence length avg       13.663333333333334\r\n",
      "--------------------------------------------------------\r\n",
      "\r\n",
      "Dataset: Validation\r\n",
      "--------------------------------------------------------\r\n",
      "Details                          Value                     \r\n",
      "========================================================\r\n",
      "Total Instance                   1000        \r\n",
      "Total Triplet Instance           1989        \r\n",
      "Triplet avg (per review)         1.989       \r\n",
      "Triplet min (per review)         0           \r\n",
      "Triplet max (per review)         14          \r\n",
      "Sequence length max              114         \r\n",
      "Sequence length min              2           \r\n",
      "Sequence length avg              15.371      \r\n",
      "Unique sequence length max       71          \r\n",
      "Unique sequence length min       2           \r\n",
      "Unique sequence length avg       13.233      \r\n",
      "--------------------------------------------------------\r\n",
      "\r\n",
      "Dataset: Testing\r\n",
      "--------------------------------------------------------\r\n",
      "Details                          Value                     \r\n",
      "========================================================\r\n",
      "Total Instance                   1000        \r\n",
      "Total Triplet Instance           2291        \r\n",
      "Triplet avg (per review)         2.291       \r\n",
      "Triplet min (per review)         0           \r\n",
      "Triplet max (per review)         15          \r\n",
      "Sequence length max              74          \r\n",
      "Sequence length min              3           \r\n",
      "Sequence length avg              15.498      \r\n",
      "Unique sequence length max       52          \r\n",
      "Unique sequence length min       3           \r\n",
      "Unique sequence length avg       13.39       \r\n",
      "--------------------------------------------------------\r\n",
      "Downloading: 100%|███████████████████████████| 945M/945M [00:44<00:00, 22.2MB/s]\r\n",
      "Epochs: 1/10: 100%|█| 375/375 [02:04<00:00,  3.01batch/s, loss=0.142, lr=0.00027\r\n",
      "Epochs: 2/10: 100%|█| 375/375 [02:03<00:00,  3.04batch/s, loss=0.257, lr=0.00024\r\n",
      "Epochs: 3/10: 100%|█| 375/375 [02:03<00:00,  3.05batch/s, loss=0.051, lr=0.00021\r\n",
      "Epochs: 4/10: 100%|█| 375/375 [02:03<00:00,  3.04batch/s, loss=0.0621, lr=0.0001\r\n",
      "Epochs: 5/10: 100%|█| 375/375 [02:03<00:00,  3.04batch/s, loss=0.0163, lr=0.0001\r\n",
      "Epochs: 6/10: 100%|█| 375/375 [02:03<00:00,  3.03batch/s, loss=0.00234, lr=0.000\r\n",
      "Epochs: 7/10: 100%|█| 375/375 [02:03<00:00,  3.04batch/s, loss=0.00544, lr=9e-5]\r\n",
      "Epochs: 8/10: 100%|█| 375/375 [02:03<00:00,  3.04batch/s, loss=0.00473, lr=6e-5]\r\n",
      "Epochs: 9/10: 100%|█| 375/375 [02:03<00:00,  3.04batch/s, loss=0.000947, lr=3e-5\r\n",
      "Epochs: 10/10: 100%|███| 375/375 [02:03<00:00,  3.04batch/s, loss=0.00158, lr=0]\r\n",
      "----------------------------------------------------------------------\r\n",
      "Epochs               Avg Training Loss     Avg Val Loss \r\n",
      "======================================================================\r\n",
      "1 / 10               0.5262986187736194    0.2166536763906479  \r\n",
      "----------------------------------------------------------------------\r\n",
      "2 / 10               0.15333918007214864   0.1634409745633602  \r\n",
      "----------------------------------------------------------------------\r\n",
      "3 / 10               0.09490882624934117   0.14855724129080772 \r\n",
      "----------------------------------------------------------------------\r\n",
      "4 / 10               0.06754625562330087   0.15525219163298606 \r\n",
      "----------------------------------------------------------------------\r\n",
      "5 / 10               0.06030607079714537   0.14496691836416722 \r\n",
      "----------------------------------------------------------------------\r\n",
      "6 / 10               0.038636534750461575  0.15367713741399347 \r\n",
      "----------------------------------------------------------------------\r\n",
      "7 / 10               0.02868352239629409   0.1555650839013979  \r\n",
      "----------------------------------------------------------------------\r\n",
      "8 / 10               0.021194796201462546  0.17352329321205617 \r\n",
      "----------------------------------------------------------------------\r\n",
      "9 / 10               0.016490887224751834  0.164697882598266   \r\n",
      "----------------------------------------------------------------------\r\n",
      "10 / 10              0.014049802666607623  0.17178936595935373 \r\n",
      "----------------------------------------------------------------------\r\n",
      "Generating: 100%|██████████████████████████| 375/375 [09:43<00:00,  1.56s/batch]\r\n",
      "100%|████████████████████████████████████| 3000/3000 [00:00<00:00, 69368.23it/s]\r\n",
      "--- Train Generation: 583.9464707374573 seconds ---\r\n",
      "Generating: 100%|██████████████████████████| 125/125 [03:14<00:00,  1.55s/batch]\r\n",
      "100%|████████████████████████████████████| 1000/1000 [00:00<00:00, 70942.00it/s]\r\n",
      "--- Test Generation: 194.41393542289734 seconds ---\r\n",
      "Generating: 100%|██████████████████████████| 125/125 [03:12<00:00,  1.54s/batch]\r\n",
      "100%|████████████████████████████████████| 1000/1000 [00:00<00:00, 73311.61it/s]\r\n",
      "--- Eval Generation: 193.13153553009033 seconds ---\r\n",
      "Train:\r\n",
      "Columns: metrics, raw_score, fixed_score\r\n",
      "\tprecision | 0.9956689124157844 | 0.992781520692974.\r\n",
      "\trecall | 0.993437900128041 | 0.9905569782330346.\r\n",
      "\tf1 | 0.9945521551033488 | 0.9916680019227688.\r\n",
      "Validation:\r\n",
      "Columns: metrics, raw_score, fixed_score\r\n",
      "\tprecision | 0.8120481927710843 | 0.8178313253012048.\r\n",
      "\trecall | 0.848012078510317 | 0.8540513336688476.\r\n",
      "\tf1 | 0.829640571147218 | 0.8355489906450024.\r\n",
      "Test:\r\n",
      "Columns: metrics, raw_score, fixed_score\r\n",
      "\tprecision | 0.8454152664426309 | 0.8516562650024004.\r\n",
      "\trecall | 0.7696678321678322 | 0.7753496503496503.\r\n",
      "\tf1 | 0.8057652711050102 | 0.8117135666895449.\r\n",
      "[[('pelayanan', 'ramah', 'positif'),\r\n",
      "  ('kamar', 'nyaman', 'positif'),\r\n",
      "  ('fasilitas', 'lengkap', 'positif'),\r\n",
      "  ('kamar showernya', 'kurang panas', 'negatif')],\r\n",
      " [],\r\n",
      " [('harga', 'terjangkau', 'positif'), ('fasilitas', 'nyaman', 'positif')],\r\n",
      " [('kamar', 'luas', 'positif'),\r\n",
      "  ('kamar', 'bersih', 'positif'),\r\n",
      "  ('seprai', 'bersih', 'positif')],\r\n",
      " [('seprai nya', 'kurang bersih', 'negatif')],\r\n",
      " [('kamarnya', 'bersih', 'positif'), ('kamarnya', 'rapi', 'positif')]]\r\n"
     ]
    }
   ],
   "source": [
    "!python main.py --prefix exp-m0-m2 --config resources/exp-v2/exp-m0.yaml --do-train --do-test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0006a3bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-19T13:26:12.370134Z",
     "iopub.status.busy": "2022-05-19T13:26:12.369318Z",
     "iopub.status.idle": "2022-05-19T14:07:08.019730Z",
     "shell.execute_reply": "2022-05-19T14:07:08.019207Z"
    },
    "papermill": {
     "duration": 2457.600366,
     "end_time": "2022-05-19T14:07:08.019889",
     "exception": false,
     "start_time": "2022-05-19T13:26:10.419523",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Namespace(config='resources/exp-v2/exp-m1.yaml', do_test=True, do_train=True, prefix='exp-m1-m3')\r\n",
      "Tokenizer type: Wikidepia/IndoT5-base\r\n",
      "unfilter\r\n",
      "\r\n",
      "Dataset: Training\r\n",
      "--------------------------------------------------------\r\n",
      "Details                          Value                     \r\n",
      "========================================================\r\n",
      "Total Instance                   3000        \r\n",
      "Total Triplet Instance           6251        \r\n",
      "Triplet avg (per review)         2.083666666666667\r\n",
      "Triplet min (per review)         0           \r\n",
      "Triplet max (per review)         14          \r\n",
      "Sequence length max              96          \r\n",
      "Sequence length min              2           \r\n",
      "Sequence length avg              15.911333333333333\r\n",
      "Unique sequence length max       73          \r\n",
      "Unique sequence length min       2           \r\n",
      "Unique sequence length avg       13.663333333333334\r\n",
      "--------------------------------------------------------\r\n",
      "\r\n",
      "Dataset: Validation\r\n",
      "--------------------------------------------------------\r\n",
      "Details                          Value                     \r\n",
      "========================================================\r\n",
      "Total Instance                   1000        \r\n",
      "Total Triplet Instance           1989        \r\n",
      "Triplet avg (per review)         1.989       \r\n",
      "Triplet min (per review)         0           \r\n",
      "Triplet max (per review)         14          \r\n",
      "Sequence length max              114         \r\n",
      "Sequence length min              2           \r\n",
      "Sequence length avg              15.371      \r\n",
      "Unique sequence length max       71          \r\n",
      "Unique sequence length min       2           \r\n",
      "Unique sequence length avg       13.233      \r\n",
      "--------------------------------------------------------\r\n",
      "\r\n",
      "Dataset: Testing\r\n",
      "--------------------------------------------------------\r\n",
      "Details                          Value                     \r\n",
      "========================================================\r\n",
      "Total Instance                   1000        \r\n",
      "Total Triplet Instance           2291        \r\n",
      "Triplet avg (per review)         2.291       \r\n",
      "Triplet min (per review)         0           \r\n",
      "Triplet max (per review)         15          \r\n",
      "Sequence length max              74          \r\n",
      "Sequence length min              3           \r\n",
      "Sequence length avg              15.498      \r\n",
      "Unique sequence length max       52          \r\n",
      "Unique sequence length min       3           \r\n",
      "Unique sequence length avg       13.39       \r\n",
      "--------------------------------------------------------\r\n",
      "Epochs: 1/10: 100%|█| 375/375 [02:04<00:00,  3.02batch/s, loss=0.142, lr=0.00027\r\n",
      "Epochs: 2/10: 100%|█| 375/375 [02:04<00:00,  3.01batch/s, loss=0.257, lr=0.00024\r\n",
      "Epochs: 3/10: 100%|█| 375/375 [02:03<00:00,  3.03batch/s, loss=0.051, lr=0.00021\r\n",
      "Epochs: 4/10: 100%|█| 375/375 [02:03<00:00,  3.03batch/s, loss=0.0621, lr=0.0001\r\n",
      "Epochs: 5/10: 100%|█| 375/375 [02:04<00:00,  3.02batch/s, loss=0.0163, lr=0.0001\r\n",
      "Epochs: 6/10: 100%|█| 375/375 [02:04<00:00,  3.02batch/s, loss=0.00234, lr=0.000\r\n",
      "Epochs: 7/10: 100%|█| 375/375 [02:03<00:00,  3.03batch/s, loss=0.00544, lr=9e-5]\r\n",
      "Epochs: 8/10: 100%|█| 375/375 [02:04<00:00,  3.01batch/s, loss=0.00473, lr=6e-5]\r\n",
      "Epochs: 9/10: 100%|█| 375/375 [02:04<00:00,  3.02batch/s, loss=0.000947, lr=3e-5\r\n",
      "Epochs: 10/10: 100%|███| 375/375 [02:03<00:00,  3.03batch/s, loss=0.00158, lr=0]\r\n",
      "----------------------------------------------------------------------\r\n",
      "Epochs               Avg Training Loss     Avg Val Loss \r\n",
      "======================================================================\r\n",
      "1 / 10               0.5262986187736194    0.2166536763906479  \r\n",
      "----------------------------------------------------------------------\r\n",
      "2 / 10               0.15333918007214864   0.1634409745633602  \r\n",
      "----------------------------------------------------------------------\r\n",
      "3 / 10               0.09490882624934117   0.14855724129080772 \r\n",
      "----------------------------------------------------------------------\r\n",
      "4 / 10               0.06754625562330087   0.15525219163298606 \r\n",
      "----------------------------------------------------------------------\r\n",
      "5 / 10               0.06030607079714537   0.14496691836416722 \r\n",
      "----------------------------------------------------------------------\r\n",
      "6 / 10               0.038636534750461575  0.15367713741399347 \r\n",
      "----------------------------------------------------------------------\r\n",
      "7 / 10               0.02868352239629409   0.1555650839013979  \r\n",
      "----------------------------------------------------------------------\r\n",
      "8 / 10               0.021194796201462546  0.17352329321205617 \r\n",
      "----------------------------------------------------------------------\r\n",
      "9 / 10               0.016490887224751834  0.164697882598266   \r\n",
      "----------------------------------------------------------------------\r\n",
      "10 / 10              0.014049802666607623  0.17178936595935373 \r\n",
      "----------------------------------------------------------------------\r\n",
      "Generating: 100%|██████████████████████████| 375/375 [10:13<00:00,  1.64s/batch]\r\n",
      "100%|████████████████████████████████████| 3000/3000 [00:00<00:00, 48415.74it/s]\r\n",
      "--- Train Generation: 614.456723690033 seconds ---\r\n",
      "Generating: 100%|██████████████████████████| 125/125 [03:20<00:00,  1.61s/batch]\r\n",
      "100%|████████████████████████████████████| 1000/1000 [00:00<00:00, 68794.04it/s]\r\n",
      "--- Test Generation: 201.16718697547913 seconds ---\r\n",
      "Generating: 100%|██████████████████████████| 125/125 [03:22<00:00,  1.62s/batch]\r\n",
      "100%|████████████████████████████████████| 1000/1000 [00:00<00:00, 76358.64it/s]\r\n",
      "--- Eval Generation: 202.3029956817627 seconds ---\r\n",
      "Train:\r\n",
      "Columns: metrics, raw_score, fixed_score\r\n",
      "\tprecision | 0.9956689124157844 | 0.992781520692974.\r\n",
      "\trecall | 0.993437900128041 | 0.9905569782330346.\r\n",
      "\tf1 | 0.9945521551033488 | 0.9916680019227688.\r\n",
      "Validation:\r\n",
      "Columns: metrics, raw_score, fixed_score\r\n",
      "\tprecision | 0.8120481927710843 | 0.8178313253012048.\r\n",
      "\trecall | 0.848012078510317 | 0.8540513336688476.\r\n",
      "\tf1 | 0.829640571147218 | 0.8355489906450024.\r\n",
      "Test:\r\n",
      "Columns: metrics, raw_score, fixed_score\r\n",
      "\tprecision | 0.8454152664426309 | 0.8516562650024004.\r\n",
      "\trecall | 0.7696678321678322 | 0.7753496503496503.\r\n",
      "\tf1 | 0.8057652711050102 | 0.8117135666895449.\r\n",
      "[[('pelayanan', 'ramah', 'positif'),\r\n",
      "  ('kamar', 'nyaman', 'positif'),\r\n",
      "  ('fasilitas', 'lengkap', 'positif'),\r\n",
      "  ('kamar showernya', 'kurang panas', 'negatif')],\r\n",
      " [],\r\n",
      " [('harga', 'terjangkau', 'positif'), ('fasilitas', 'nyaman', 'positif')],\r\n",
      " [('kamar', 'luas', 'positif'),\r\n",
      "  ('kamar', 'bersih', 'positif'),\r\n",
      "  ('seprai', 'bersih', 'positif')],\r\n",
      " [('seprai nya', 'kurang bersih', 'negatif')],\r\n",
      " [('kamarnya', 'bersih', 'positif'), ('kamarnya', 'rapi', 'positif')]]\r\n"
     ]
    }
   ],
   "source": [
    "!python main.py --prefix exp-m1-m3 --config resources/exp-v2/exp-m1.yaml --do-train --do-test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6a683368",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-19T14:07:14.581999Z",
     "iopub.status.busy": "2022-05-19T14:07:14.579216Z",
     "iopub.status.idle": "2022-05-19T14:51:18.484927Z",
     "shell.execute_reply": "2022-05-19T14:51:18.482482Z"
    },
    "papermill": {
     "duration": 2647.342756,
     "end_time": "2022-05-19T14:51:18.485070",
     "exception": false,
     "start_time": "2022-05-19T14:07:11.142314",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Namespace(config='resources/exp-v2/exp-m4.yaml', do_test=True, do_train=True, prefix='exp-m4')\r\n",
      "Tokenizer type: Wikidepia/IndoT5-base\r\n",
      "unfilter\r\n",
      "\r\n",
      "Dataset: Training\r\n",
      "--------------------------------------------------------\r\n",
      "Details                          Value                     \r\n",
      "========================================================\r\n",
      "Total Instance                   3000        \r\n",
      "Total Triplet Instance           6251        \r\n",
      "Triplet avg (per review)         2.083666666666667\r\n",
      "Triplet min (per review)         0           \r\n",
      "Triplet max (per review)         14          \r\n",
      "Sequence length max              96          \r\n",
      "Sequence length min              2           \r\n",
      "Sequence length avg              15.911333333333333\r\n",
      "Unique sequence length max       73          \r\n",
      "Unique sequence length min       2           \r\n",
      "Unique sequence length avg       13.663333333333334\r\n",
      "--------------------------------------------------------\r\n",
      "\r\n",
      "Dataset: Validation\r\n",
      "--------------------------------------------------------\r\n",
      "Details                          Value                     \r\n",
      "========================================================\r\n",
      "Total Instance                   1000        \r\n",
      "Total Triplet Instance           1989        \r\n",
      "Triplet avg (per review)         1.989       \r\n",
      "Triplet min (per review)         0           \r\n",
      "Triplet max (per review)         14          \r\n",
      "Sequence length max              114         \r\n",
      "Sequence length min              2           \r\n",
      "Sequence length avg              15.371      \r\n",
      "Unique sequence length max       71          \r\n",
      "Unique sequence length min       2           \r\n",
      "Unique sequence length avg       13.233      \r\n",
      "--------------------------------------------------------\r\n",
      "\r\n",
      "Dataset: Testing\r\n",
      "--------------------------------------------------------\r\n",
      "Details                          Value                     \r\n",
      "========================================================\r\n",
      "Total Instance                   1000        \r\n",
      "Total Triplet Instance           2291        \r\n",
      "Triplet avg (per review)         2.291       \r\n",
      "Triplet min (per review)         0           \r\n",
      "Triplet max (per review)         15          \r\n",
      "Sequence length max              74          \r\n",
      "Sequence length min              3           \r\n",
      "Sequence length avg              15.498      \r\n",
      "Unique sequence length max       52          \r\n",
      "Unique sequence length min       3           \r\n",
      "Unique sequence length avg       13.39       \r\n",
      "--------------------------------------------------------\r\n",
      "Epochs: 1/10: 100%|█| 375/375 [02:04<00:00,  3.02batch/s, loss=0.142, lr=0.00027\r\n",
      "Epochs: 2/10: 100%|█| 375/375 [02:03<00:00,  3.03batch/s, loss=0.257, lr=0.00024\r\n",
      "Epochs: 3/10: 100%|█| 375/375 [02:04<00:00,  3.01batch/s, loss=0.051, lr=0.00021\r\n",
      "Epochs: 4/10: 100%|█| 375/375 [02:04<00:00,  3.02batch/s, loss=0.0621, lr=0.0001\r\n",
      "Epochs: 5/10: 100%|█| 375/375 [02:03<00:00,  3.04batch/s, loss=0.0163, lr=0.0001\r\n",
      "Epochs: 6/10: 100%|█| 375/375 [02:04<00:00,  3.00batch/s, loss=0.00234, lr=0.000\r\n",
      "Epochs: 7/10: 100%|█| 375/375 [02:03<00:00,  3.04batch/s, loss=0.00544, lr=9e-5]\r\n",
      "Epochs: 8/10: 100%|█| 375/375 [02:03<00:00,  3.03batch/s, loss=0.00473, lr=6e-5]\r\n",
      "Epochs: 9/10: 100%|█| 375/375 [02:03<00:00,  3.03batch/s, loss=0.000947, lr=3e-5\r\n",
      "Epochs: 10/10: 100%|███| 375/375 [02:03<00:00,  3.04batch/s, loss=0.00158, lr=0]\r\n",
      "----------------------------------------------------------------------\r\n",
      "Epochs               Avg Training Loss     Avg Val Loss \r\n",
      "======================================================================\r\n",
      "1 / 10               0.5262986187736194    0.2166536763906479  \r\n",
      "----------------------------------------------------------------------\r\n",
      "2 / 10               0.15333918007214864   0.1634409745633602  \r\n",
      "----------------------------------------------------------------------\r\n",
      "3 / 10               0.09490882624934117   0.14855724129080772 \r\n",
      "----------------------------------------------------------------------\r\n",
      "4 / 10               0.06754625562330087   0.15525219163298606 \r\n",
      "----------------------------------------------------------------------\r\n",
      "5 / 10               0.06030607079714537   0.14496691836416722 \r\n",
      "----------------------------------------------------------------------\r\n",
      "6 / 10               0.038636534750461575  0.15367713741399347 \r\n",
      "----------------------------------------------------------------------\r\n",
      "7 / 10               0.02868352239629409   0.1555650839013979  \r\n",
      "----------------------------------------------------------------------\r\n",
      "8 / 10               0.021194796201462546  0.17352329321205617 \r\n",
      "----------------------------------------------------------------------\r\n",
      "9 / 10               0.016490887224751834  0.164697882598266   \r\n",
      "----------------------------------------------------------------------\r\n",
      "10 / 10              0.014049802666607623  0.17178936595935373 \r\n",
      "----------------------------------------------------------------------\r\n",
      "Generating: 100%|██████████████████████████| 375/375 [09:52<00:00,  1.58s/batch]\r\n",
      "100%|████████████████████████████████████| 3000/3000 [00:00<00:00, 72696.62it/s]\r\n",
      "--- Train Generation: 723.6570529937744 seconds ---\r\n",
      "Generating: 100%|██████████████████████████| 125/125 [03:17<00:00,  1.58s/batch]\r\n",
      "100%|████████████████████████████████████| 1000/1000 [00:00<00:00, 67995.53it/s]\r\n",
      "--- Test Generation: 239.8222405910492 seconds ---\r\n",
      "Generating: 100%|██████████████████████████| 125/125 [03:16<00:00,  1.57s/batch]\r\n",
      "100%|████████████████████████████████████| 1000/1000 [00:00<00:00, 72629.90it/s]\r\n",
      "--- Eval Generation: 239.91212701797485 seconds ---\r\n",
      "Train:\r\n",
      "Columns: metrics, raw_score, fixed_score\r\n",
      "\tprecision | 0.9956689124157844 | 0.9926211100417067.\r\n",
      "\trecall | 0.993437900128041 | 0.9903969270166453.\r\n",
      "\tf1 | 0.9945521551033488 | 0.9915077711905144.\r\n",
      "Validation:\r\n",
      "Columns: metrics, raw_score, fixed_score\r\n",
      "\tprecision | 0.8120481927710843 | 0.8173493975903614.\r\n",
      "\trecall | 0.848012078510317 | 0.8535480624056366.\r\n",
      "\tf1 | 0.829640571147218 | 0.8350566223535205.\r\n",
      "Test:\r\n",
      "Columns: metrics, raw_score, fixed_score\r\n",
      "\tprecision | 0.8454152664426309 | 0.8506961113778204.\r\n",
      "\trecall | 0.7696678321678322 | 0.7744755244755245.\r\n",
      "\tf1 | 0.8057652711050102 | 0.8107984442919239.\r\n",
      "[[('pelayanan', 'ramah', 'positif'),\r\n",
      "  ('kamar', 'nyaman', 'positif'),\r\n",
      "  ('fasilitas', 'lengkap', 'positif'),\r\n",
      "  ('airnya showernya', 'kurang panas', 'negatif')],\r\n",
      " [],\r\n",
      " [('harga', 'terjangkau', 'positif'), ('fasilitas', 'nyaman', 'positif')],\r\n",
      " [('kamar', 'luas', 'positif'),\r\n",
      "  ('kamar', 'bersih', 'positif'),\r\n",
      "  ('seprai', 'bersih', 'positif')],\r\n",
      " [('seprai nya', 'kurang bersih', 'negatif')],\r\n",
      " [('kamarnya', 'bersih', 'positif'), ('kamarnya', 'rapi', 'positif')]]\r\n"
     ]
    }
   ],
   "source": [
    "!python main.py --prefix exp-m4 --config resources/exp-v2/exp-m4.yaml --do-train --do-test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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     "iopub.status.idle": "2022-05-19T15:35:47.839337Z",
     "shell.execute_reply": "2022-05-19T15:35:47.837349Z"
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     "status": "completed"
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     "text": [
      "Namespace(config='resources/exp-v2/exp-m5.yaml', do_test=True, do_train=True, prefix='exp-m5')\r\n",
      "Tokenizer type: Wikidepia/IndoT5-base\r\n",
      "unfilter\r\n",
      "\r\n",
      "Dataset: Training\r\n",
      "--------------------------------------------------------\r\n",
      "Details                          Value                     \r\n",
      "========================================================\r\n",
      "Total Instance                   3000        \r\n",
      "Total Triplet Instance           6251        \r\n",
      "Triplet avg (per review)         2.083666666666667\r\n",
      "Triplet min (per review)         0           \r\n",
      "Triplet max (per review)         14          \r\n",
      "Sequence length max              96          \r\n",
      "Sequence length min              2           \r\n",
      "Sequence length avg              15.911333333333333\r\n",
      "Unique sequence length max       73          \r\n",
      "Unique sequence length min       2           \r\n",
      "Unique sequence length avg       13.663333333333334\r\n",
      "--------------------------------------------------------\r\n",
      "\r\n",
      "Dataset: Validation\r\n",
      "--------------------------------------------------------\r\n",
      "Details                          Value                     \r\n",
      "========================================================\r\n",
      "Total Instance                   1000        \r\n",
      "Total Triplet Instance           1989        \r\n",
      "Triplet avg (per review)         1.989       \r\n",
      "Triplet min (per review)         0           \r\n",
      "Triplet max (per review)         14          \r\n",
      "Sequence length max              114         \r\n",
      "Sequence length min              2           \r\n",
      "Sequence length avg              15.371      \r\n",
      "Unique sequence length max       71          \r\n",
      "Unique sequence length min       2           \r\n",
      "Unique sequence length avg       13.233      \r\n",
      "--------------------------------------------------------\r\n",
      "\r\n",
      "Dataset: Testing\r\n",
      "--------------------------------------------------------\r\n",
      "Details                          Value                     \r\n",
      "========================================================\r\n",
      "Total Instance                   1000        \r\n",
      "Total Triplet Instance           2291        \r\n",
      "Triplet avg (per review)         2.291       \r\n",
      "Triplet min (per review)         0           \r\n",
      "Triplet max (per review)         15          \r\n",
      "Sequence length max              74          \r\n",
      "Sequence length min              3           \r\n",
      "Sequence length avg              15.498      \r\n",
      "Unique sequence length max       52          \r\n",
      "Unique sequence length min       3           \r\n",
      "Unique sequence length avg       13.39       \r\n",
      "--------------------------------------------------------\r\n",
      "Epochs: 1/10: 100%|█| 375/375 [02:04<00:00,  3.02batch/s, loss=0.142, lr=0.00027\r\n",
      "Epochs: 2/10: 100%|█| 375/375 [02:03<00:00,  3.04batch/s, loss=0.257, lr=0.00024\r\n",
      "Epochs: 3/10: 100%|█| 375/375 [02:03<00:00,  3.03batch/s, loss=0.051, lr=0.00021\r\n",
      "Epochs: 4/10: 100%|█| 375/375 [02:03<00:00,  3.03batch/s, loss=0.0621, lr=0.0001\r\n",
      "Epochs: 5/10: 100%|█| 375/375 [02:04<00:00,  3.02batch/s, loss=0.0163, lr=0.0001\r\n",
      "Epochs: 6/10: 100%|█| 375/375 [02:04<00:00,  3.02batch/s, loss=0.00234, lr=0.000\r\n",
      "Epochs: 7/10: 100%|█| 375/375 [02:03<00:00,  3.03batch/s, loss=0.00544, lr=9e-5]\r\n",
      "Epochs: 8/10: 100%|█| 375/375 [02:03<00:00,  3.03batch/s, loss=0.00473, lr=6e-5]\r\n",
      "Epochs: 9/10: 100%|█| 375/375 [02:03<00:00,  3.03batch/s, loss=0.000947, lr=3e-5\r\n",
      "Epochs: 10/10: 100%|███| 375/375 [02:03<00:00,  3.03batch/s, loss=0.00158, lr=0]\r\n",
      "----------------------------------------------------------------------\r\n",
      "Epochs               Avg Training Loss     Avg Val Loss \r\n",
      "======================================================================\r\n",
      "1 / 10               0.5262986187736194    0.2166536763906479  \r\n",
      "----------------------------------------------------------------------\r\n",
      "2 / 10               0.15333918007214864   0.1634409745633602  \r\n",
      "----------------------------------------------------------------------\r\n",
      "3 / 10               0.09490882624934117   0.14855724129080772 \r\n",
      "----------------------------------------------------------------------\r\n",
      "4 / 10               0.06754625562330087   0.15525219163298606 \r\n",
      "----------------------------------------------------------------------\r\n",
      "5 / 10               0.06030607079714537   0.14496691836416722 \r\n",
      "----------------------------------------------------------------------\r\n",
      "6 / 10               0.038636534750461575  0.15367713741399347 \r\n",
      "----------------------------------------------------------------------\r\n",
      "7 / 10               0.02868352239629409   0.1555650839013979  \r\n",
      "----------------------------------------------------------------------\r\n",
      "8 / 10               0.021194796201462546  0.17352329321205617 \r\n",
      "----------------------------------------------------------------------\r\n",
      "9 / 10               0.016490887224751834  0.164697882598266   \r\n",
      "----------------------------------------------------------------------\r\n",
      "10 / 10              0.014049802666607623  0.17178936595935373 \r\n",
      "----------------------------------------------------------------------\r\n",
      "Generating: 100%|██████████████████████████| 375/375 [10:06<00:00,  1.62s/batch]\r\n",
      "100%|████████████████████████████████████| 3000/3000 [00:00<00:00, 56426.37it/s]\r\n",
      "--- Train Generation: 737.2174429893494 seconds ---\r\n",
      "Generating: 100%|██████████████████████████| 125/125 [03:20<00:00,  1.60s/batch]\r\n",
      "100%|████████████████████████████████████| 1000/1000 [00:00<00:00, 66541.40it/s]\r\n",
      "--- Test Generation: 241.61301112174988 seconds ---\r\n",
      "Generating: 100%|██████████████████████████| 125/125 [03:19<00:00,  1.60s/batch]\r\n",
      "100%|████████████████████████████████████| 1000/1000 [00:00<00:00, 75446.62it/s]\r\n",
      "--- Eval Generation: 243.28297448158264 seconds ---\r\n",
      "Train:\r\n",
      "Columns: metrics, raw_score, fixed_score\r\n",
      "\tprecision | 0.9956689124157844 | 0.9926211100417067.\r\n",
      "\trecall | 0.993437900128041 | 0.9903969270166453.\r\n",
      "\tf1 | 0.9945521551033488 | 0.9915077711905144.\r\n",
      "Validation:\r\n",
      "Columns: metrics, raw_score, fixed_score\r\n",
      "\tprecision | 0.8120481927710843 | 0.8173493975903614.\r\n",
      "\trecall | 0.848012078510317 | 0.8535480624056366.\r\n",
      "\tf1 | 0.829640571147218 | 0.8350566223535205.\r\n",
      "Test:\r\n",
      "Columns: metrics, raw_score, fixed_score\r\n",
      "\tprecision | 0.8454152664426309 | 0.8506961113778204.\r\n",
      "\trecall | 0.7696678321678322 | 0.7744755244755245.\r\n",
      "\tf1 | 0.8057652711050102 | 0.8107984442919239.\r\n",
      "[[('pelayanan', 'ramah', 'positif'),\r\n",
      "  ('kamar', 'nyaman', 'positif'),\r\n",
      "  ('fasilitas', 'lengkap', 'positif'),\r\n",
      "  ('airnya showernya', 'kurang panas', 'negatif')],\r\n",
      " [],\r\n",
      " [('harga', 'terjangkau', 'positif'), ('fasilitas', 'nyaman', 'positif')],\r\n",
      " [('kamar', 'luas', 'positif'),\r\n",
      "  ('kamar', 'bersih', 'positif'),\r\n",
      "  ('seprai', 'bersih', 'positif')],\r\n",
      " [('seprai nya', 'kurang bersih', 'negatif')],\r\n",
      " [('kamarnya', 'bersih', 'positif'), ('kamarnya', 'rapi', 'positif')]]\r\n"
     ]
    }
   ],
   "source": [
    "!python main.py --prefix exp-m5 --config resources/exp-v2/exp-m5.yaml --do-train --do-test"
   ]
  }
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