{"cells":[{"cell_type":"markdown","metadata":{"id":"BGKCW074dTy2"},"source":["# Harvard USPTO Dataset Training"]},{"cell_type":"markdown","metadata":{"id":"6IttmojFdTy4"},"source":["## Preprocessing USPTO Data"]},{"cell_type":"markdown","source":["### Importing the Dataset\n","\n","We first need to import the actual USPTO dataset."],"metadata":{"id":"rJ6oNXYiOtC3"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1UCFBK0OdTy5","executionInfo":{"status":"ok","timestamp":1682021338971,"user_tz":240,"elapsed":13759,"user":{"displayName":"Ryan Kim","userId":"18356277368138721144"}},"outputId":"87e553e1-6593-4b2d-e578-2a4e4e742d9b"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting datasets\n","  Downloading datasets-2.11.0-py3-none-any.whl (468 kB)\n","\u001b[2K     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/usr/local/lib/python3.9/dist-packages (from pandas->datasets) (2022.7.1)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n","Installing collected packages: xxhash, multidict, frozenlist, dill, async-timeout, yarl, responses, multiprocess, huggingface-hub, aiosignal, aiohttp, datasets\n","Successfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 datasets-2.11.0 dill-0.3.6 frozenlist-1.3.3 huggingface-hub-0.13.4 multidict-6.0.4 multiprocess-0.70.14 responses-0.18.0 xxhash-3.2.0 yarl-1.8.2\n"]}],"source":["!pip install datasets"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"V20AfVn8dTy6"},"outputs":[],"source":["from datasets import load_dataset\n","import pandas as pd\n","import numpy as np\n","import os\n","import json\n","import torch\n","import sys"]},{"cell_type":"markdown","metadata":{"id":"DALhUYBydTy7"},"source":["### Loading the Dataset\n","\n","We need to extract the dataset. We filter only for those in January 2016."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":474,"referenced_widgets":["9f321834b0bc4bc1ac089f9813fc0fb1","12e47c63e2fb4596b645c252e9756899","043c0260b1e14a129feab90cff5ef099","ddba4a3380794815a732bd1a453c925c","8d6813d2f8ec401d85e599eadc8dc093","4dca0683fdc5459e88f6687f3196af7f","0dba55c366e44537adf4048c24391786","6695e22c05d548139e71029524c0bc68","2a7b1a0f1e94446bb343979d86264f0c","155a8f30bf6f4bbcb491dda1ad722c3b","aa0b1c2f45104fc5b59e1704bce27e5f","06a520a78f5d4b95982a476b29734cbf","c5be3bfd001346af8d33caf0a64efc7f","de1f71b45aba42e4a807100c4a8bf81f","6eee3b7c1ce9453e8c73f568537d5ac8","8f895536b733460d9ad2987333e3733b","e05d3a3d645948a3b2745cac3d5aa737","da25e097845440948f1d3ec1096f2a99","5cfa97b42072490f8ab4d5f60e0a1792","22f78b57f5604f928475f9a81d723baf","87b97444ca194dd18f1a5bd6e4082fb1","de9855ff33fb41b58ef21fbb26f81b85","9a74f42973b34203af1100702e07c28f","08d60a39b36942c184664157f738c5f0","fc886230dc454a72a3b2954f9818e9e8","482f58d514734fd2aa1d82693c7d5c34","f52a0089a4494e719c54289ff33a1c04","c2f38dc161184b16ba930c2362bc8e3b","e0f8f41cd81647908c645f76483148e5","2326d9dbc48b40769dcde81195324f40","0887166927dd4805ae3346c3158be0d2","04f4e59cfdfe492c9644dd99f46910e9","5cfce12745f44e5a9c94b72120170915","d7bfd7f1e624447a9c1eea68116d915b","6a337183787b43ec8634dc0c5b95bd72","7dd3444c8a754737b4ed1c8f64f68601","d9c18d34c6b249b78fcbace6d79cbdec","52879a2ff4864ba084a15fe02ab8b1fa","a90a02a84860461789ad04c386cdf44e","8cc2a2fe26534066a3a778704cc5984b","754c5121d01646c4aa4284df0eb9bb4f","b957825a21c2412a9b0101869d1f44d8","9f145dfa3f6347a7ab46aaa5fb294ea1","85d3c680e7424ab99c5153f315ac51a0","18d0fb273b444854a97ddb941859e0bc","fedd62c855344bbc931485f1bed1bb20","3cd64accf6104e5f8a3ad59381650a61","d39610354db4493f9f33405267b50179","c82c7f9596a746b6a91435ddfe2801f5","453092a5a76540f2b7561c3f4f84f8b2","ba82d11406a2498ba8948b5729fb4935","50863c3f09e343a9970b73dc70abba90","269546507c224a049088863d503401b1","0cd095c293104c67996f52685a29b7e9","f4c9a13af4cd478bacedd30ee2d81b8d","f9ef1b3c4aa54426af822c5f8420f2ca","28f10c7180d24373ad411015ef51d68c","59139bbd088c4804a9c8213afc3ddf21","a6c68f2b636b4b10ab7f846a789b00aa","417d14969aa141c885ae6ddd6b554324","345e90b85bb542168925cc014a1780ff","0c2447ec9c2346feb9d9d34ad6f5ec89","2c3ac49e5ace478ea09109090519aa65","984011a4cf53494baca77e2847c1a6ec","0ad51ba66c3a48a2bf44a58c63d6f6b8","f71dc5f4ba95461eafff245393c29efb","422a18bc728147cfab41a3d434784b87","8e84ffdf79144dee93ec82a857d8abf8","a6de06c9f8c9494abd2c5146a151abf4","b707d410fc0b46be82513a3156c864ec","7cdeb5f8b21f42549d6919f0a140697a","8fe2841c26c947c28479cd459ae5edff","d40cdc8dac2b40429bd92e6330916ed5","f8c2274beb1d47e1a2e8d76d3f2babe1","a0ade24bceb54a7daedc48858588590a","895d67eeac0f46edb926a8bdd33f419b","21d8d59ca7304455aae43a23c7cbbbe8"]},"id":"d-bfQ8MsdTy8","executionInfo":{"status":"ok","timestamp":1682021405537,"user_tz":240,"elapsed":56565,"user":{"displayName":"Ryan Kim","userId":"18356277368138721144"}},"outputId":"734dcb4b-d924-479d-909c-ef907c2284c8"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/14.7k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9f321834b0bc4bc1ac089f9813fc0fb1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading readme:   0%|          | 0.00/10.9k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"06a520a78f5d4b95982a476b29734cbf"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Downloading and preparing dataset hupd/sample to /root/.cache/huggingface/datasets/HUPD___hupd/sample-a4eeba92b4229e93/0.0.0/6920d2def8fd7767046c0470603357f76866e5a09c97e19571896bfdca521142...\n","Loading dataset with config: PatentsConfig(name='sample', version=0.0.0, data_dir='sample', data_files={'train': ['https://huggingface.co/datasets/HUPD/hupd/blob/main/hupd_metadata_2022-02-22.feather']}, description='Patent data from January 2016, for debugging')\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/6.67M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9a74f42973b34203af1100702e07c28f"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Using metadata file: /root/.cache/huggingface/datasets/downloads/bac34b767c2799633010fa78ecd401d2eeffd62eff58abdb4db75829f8932710\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/388M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d7bfd7f1e624447a9c1eea68116d915b"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Reading metadata file: /root/.cache/huggingface/datasets/downloads/bac34b767c2799633010fa78ecd401d2eeffd62eff58abdb4db75829f8932710\n","Filtering train dataset by filing start date: 2016-01-01\n","Filtering train dataset by filing end date: 2016-01-21\n","Filtering val dataset by filing start date: 2016-01-22\n","Filtering val dataset by filing end date: 2016-01-31\n"]},{"output_type":"display_data","data":{"text/plain":["Generating train split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"18d0fb273b444854a97ddb941859e0bc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating validation split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f9ef1b3c4aa54426af822c5f8420f2ca"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dataset hupd downloaded and prepared to /root/.cache/huggingface/datasets/HUPD___hupd/sample-a4eeba92b4229e93/0.0.0/6920d2def8fd7767046c0470603357f76866e5a09c97e19571896bfdca521142. Subsequent calls will reuse this data.\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"422a18bc728147cfab41a3d434784b87"}},"metadata":{}}],"source":["dataset_dict = load_dataset('HUPD/hupd',\n","    name='sample',\n","    data_files=\"https://huggingface.co/datasets/HUPD/hupd/blob/main/hupd_metadata_2022-02-22.feather\", \n","    icpr_label=None,\n","    train_filing_start_date='2016-01-01',\n","    train_filing_end_date='2016-01-21',\n","    val_filing_start_date='2016-01-22',\n","    val_filing_end_date='2016-01-31',\n",")"]},{"cell_type":"markdown","metadata":{"id":"No0GXCF9dTy8"},"source":["We print out the dataset to understand what exactly we want to look for"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2vAINVw8dTy9","executionInfo":{"status":"ok","timestamp":1682021405538,"user_tz":240,"elapsed":11,"user":{"displayName":"Ryan Kim","userId":"18356277368138721144"}},"outputId":"49141a02-dbe3-40ea-e035-1b8a8034940e"},"outputs":[{"output_type":"stream","name":"stdout","text":["DatasetDict({\n","    train: Dataset({\n","        features: ['patent_number', 'decision', 'title', 'abstract', 'claims', 'background', 'summary', 'description', 'cpc_label', 'ipc_label', 'filing_date', 'patent_issue_date', 'date_published', 'examiner_id'],\n","        num_rows: 16153\n","    })\n","    validation: Dataset({\n","        features: ['patent_number', 'decision', 'title', 'abstract', 'claims', 'background', 'summary', 'description', 'cpc_label', 'ipc_label', 'filing_date', 'patent_issue_date', 'date_published', 'examiner_id'],\n","        num_rows: 9094\n","    })\n","})\n"]}],"source":["print(dataset_dict)"]},{"cell_type":"markdown","metadata":{"id":"nH-s94K4dTy-"},"source":["We separate our data between training and validation"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"y8G0zXJbdTy_"},"outputs":[],"source":["df_train = pd.DataFrame(dataset_dict['train'] )\n","df_val = pd.DataFrame(dataset_dict['validation'] )"]},{"cell_type":"markdown","metadata":{"id":"KZwFSLdydTzB"},"source":["### Pre-Processing the Data\n","\n","We are interested in the following columns:\n","- Patent Number <- purely for documentation purposes\n","- Abstract\n","- Claims\n","- Decision <- our `y`\n","\n","Let's preprocess them both out of our training and validation data\n","\n","Also, consider that the \"Decision\" column has three types of values: \"Accepted\", \"Rejected\", and \"Pending\". To remove unecessary baggage, we will be only looking for \"Accepted\" and \"Rejected\"."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oEGYVesedTzB"},"outputs":[],"source":["necessary_columns = [\"patent_number\",\"abstract\",\"claims\",\"decision\"]\n","output_values = ['ACCEPTED','REJECTED'] "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0mMznxq7dTzC"},"outputs":[],"source":["trainFeaturesToDrop = [col for col in list(df_train.columns) if col not in necessary_columns]\n","trainDF = df_train.dropna()\n","trainDF.drop(columns=trainFeaturesToDrop, inplace=True)\n","trainDF = trainDF[trainDF['decision'].isin(output_values)]"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":641},"id":"6jS-NnaqdTzC","executionInfo":{"status":"ok","timestamp":1682021420941,"user_tz":240,"elapsed":6,"user":{"displayName":"Ryan Kim","userId":"18356277368138721144"}},"outputId":"5e4ee638-d7bc-4fb6-ab75-cd5ecd3d436d"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["      patent_number  decision  \\\n","0          13261748  ACCEPTED   \n","1          13995128  ACCEPTED   \n","3          14348792  ACCEPTED   \n","4          14360978  REJECTED   \n","5          14369795  ACCEPTED   \n","...             ...       ...   \n","16144      15002390  ACCEPTED   \n","16145      15002391  ACCEPTED   \n","16148      15002394  ACCEPTED   \n","16149      15002396  REJECTED   \n","16150      15330955  REJECTED   \n","\n","                                                abstract  \\\n","0      The present invention relates to passive optic...   \n","1      Embodiments of the invention provide a method ...   \n","3      A crystal growth furnace comprising a crucible...   \n","4      A shoe midsole is composed of a base plate (1)...   \n","5      A ratchet tool includes a shaft member, a hand...   \n","...                                                  ...   \n","16144  A wavelength tunable laser device, including: ...   \n","16145  In one aspect, a method for use in preparing a...   \n","16148  A robot hand controlling method executes calcu...   \n","16149  A fusion protein is disclosed. The fusion prot...   \n","16150  A pipe extraction tool that grips the inside o...   \n","\n","                                                  claims  \n","0      1. A compact optical network terminal, compris...  \n","1      1. A method comprising: using a first reader t...  \n","3      1. A crystal growth furnace for growing a crys...  \n","4      1. A sole member of footwear comprising a base...  \n","5      1. A ratchet tool, comprising a shaft member, ...  \n","...                                                  ...  \n","16144  1. A wavelength tunable laser device, comprisi...  \n","16145  1. (canceled) 2. The method of claim 19, where...  \n","16148  1. A controlling method of a robot hand, the r...  \n","16149  1. A fusion protein comprising an Fc fragment ...  \n","16150  1. A pipe extraction tool for extracting a pip...  \n","\n","[8719 rows x 4 columns]"],"text/html":["\n","  <div id=\"df-2498cab4-b5f5-4a3f-a55a-91ef75bd67d6\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>patent_number</th>\n","      <th>decision</th>\n","      <th>abstract</th>\n","      <th>claims</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>13261748</td>\n","      <td>ACCEPTED</td>\n","      <td>The present invention relates to passive optic...</td>\n","      <td>1. 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A ratchet tool, comprising a shaft member, ...</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>16144</th>\n","      <td>15002390</td>\n","      <td>ACCEPTED</td>\n","      <td>A wavelength tunable laser device, including: ...</td>\n","      <td>1. A wavelength tunable laser device, comprisi...</td>\n","    </tr>\n","    <tr>\n","      <th>16145</th>\n","      <td>15002391</td>\n","      <td>ACCEPTED</td>\n","      <td>In one aspect, a method for use in preparing a...</td>\n","      <td>1. (canceled) 2. The method of claim 19, where...</td>\n","    </tr>\n","    <tr>\n","      <th>16148</th>\n","      <td>15002394</td>\n","      <td>ACCEPTED</td>\n","      <td>A robot hand controlling method executes calcu...</td>\n","      <td>1. 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The scan d...   \n","...                                                 ...   \n","9085  The non-rigid gate device as described may be ...   \n","9090  The present invention provides an improved unc...   \n","9091  A method for detecting a software-race conditi...   \n","9092  The present application relates to multi-stage...   \n","9093  A paper feeder includes a housing, a driving u...   \n","\n","                                                 claims  \n","0     1. A treatment regimen comprising: cleansing a...  \n","1     1. A clamp arrangement for supporting a fractu...  \n","2     1-20. (canceled) 21. A mobile device comprisin...  \n","4     1. A method, comprising: executing, by one or ...  \n","9     1. A scan driving circuit for driving a scan l...  \n","...                                                 ...  \n","9085  1; A non-rigid blocking apparatus referred to ...  \n","9090  1. A method for rendering a plastic surface am...  \n","9091  1. A method for detecting a software-race cond...  \n","9092  1. A multi-stage amplitude modulation-based me...  \n","9093  1. A paper feeder, comprising: a housing; a dr...  \n","\n","[4888 rows x 4 columns]"],"text/html":["\n","  <div id=\"df-f6f537bb-f0af-45e4-95e3-0c629d839227\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>patent_number</th>\n","      <th>decision</th>\n","      <th>abstract</th>\n","      <th>claims</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>13144833</td>\n","      <td>REJECTED</td>\n","      <td>Regimen for the treatment of rosacea include t...</td>\n","      <td>1. 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We will set \"ACCEPTED\" as `1` and \"REJECTED\" as `0`."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CBxfqBL0dTzD"},"outputs":[],"source":["yKey = {\"ACCEPTED\":1,\"REJECTED\":0}"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"82I6gVrtdTzE"},"outputs":[],"source":["trainDF2 = trainDF.replace({\"decision\": yKey})\n","valDF2 = valDF.replace({\"decision\": yKey})"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":746},"id":"1XVwvlGKdTzE","executionInfo":{"status":"ok","timestamp":1682021428511,"user_tz":240,"elapsed":5,"user":{"displayName":"Ryan Kim","userId":"18356277368138721144"}},"outputId":"bb49c208-ee63-4a2c-86b1-6bea0449b583"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["      patent_number  decision  \\\n","0          13261748         1   \n","1          13995128         1   \n","3          14348792         1   \n","4          14360978         0   \n","5          14369795         1   \n","...             ...       ...   \n","16144      15002390         1   \n","16145      15002391         1   \n","16148      15002394         1   \n","16149      15002396         0   \n","16150      15330955         0   \n","\n","                                                abstract  \\\n","0      The present invention relates to passive optic...   \n","1      Embodiments of the invention provide a method ...   \n","3      A crystal growth furnace comprising a crucible...   \n","4      A shoe midsole is composed of a base plate (1)...   \n","5      A ratchet tool includes a shaft member, a hand...   \n","...                                                  ...   \n","16144  A wavelength tunable laser device, including: ...   \n","16145  In one aspect, a method for use in preparing a...   \n","16148  A robot hand controlling method executes calcu...   \n","16149  A fusion protein is disclosed. The fusion prot...   \n","16150  A pipe extraction tool that grips the inside o...   \n","\n","                                                  claims  \n","0      1. A compact optical network terminal, compris...  \n","1      1. A method comprising: using a first reader t...  \n","3      1. A crystal growth furnace for growing a crys...  \n","4      1. A sole member of footwear comprising a base...  \n","5      1. A ratchet tool, comprising a shaft member, ...  \n","...                                                  ...  \n","16144  1. A wavelength tunable laser device, comprisi...  \n","16145  1. (canceled) 2. The method of claim 19, where...  \n","16148  1. A controlling method of a robot hand, the r...  \n","16149  1. A fusion protein comprising an Fc fragment ...  \n","16150  1. A pipe extraction tool for extracting a pip...  \n","\n","[8719 rows x 4 columns]"],"text/html":["\n","  <div id=\"df-fcb55bbd-c720-4b17-82e1-4059c671fb2f\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>patent_number</th>\n","      <th>decision</th>\n","      <th>abstract</th>\n","      <th>claims</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>13261748</td>\n","      <td>1</td>\n","      <td>The present invention relates to passive optic...</td>\n","      <td>1. A compact optical network terminal, compris...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>13995128</td>\n","      <td>1</td>\n","      <td>Embodiments of the invention provide a method ...</td>\n","      <td>1. A method comprising: using a first reader t...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>14348792</td>\n","      <td>1</td>\n","      <td>A crystal growth furnace comprising a crucible...</td>\n","      <td>1. A crystal growth furnace for growing a crys...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>14360978</td>\n","      <td>0</td>\n","      <td>A shoe midsole is composed of a base plate (1)...</td>\n","      <td>1. A sole member of footwear comprising a base...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>14369795</td>\n","      <td>1</td>\n","      <td>A ratchet tool includes a shaft member, a hand...</td>\n","      <td>1. 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The scan d...   \n","...                                                 ...   \n","9085  The non-rigid gate device as described may be ...   \n","9090  The present invention provides an improved unc...   \n","9091  A method for detecting a software-race conditi...   \n","9092  The present application relates to multi-stage...   \n","9093  A paper feeder includes a housing, a driving u...   \n","\n","                                                 claims  \n","0     1. A treatment regimen comprising: cleansing a...  \n","1     1. A clamp arrangement for supporting a fractu...  \n","2     1-20. (canceled) 21. A mobile device comprisin...  \n","4     1. A method, comprising: executing, by one or ...  \n","9     1. A scan driving circuit for driving a scan l...  \n","...                                                 ...  \n","9085  1; A non-rigid blocking apparatus referred to ...  \n","9090  1. A method for rendering a plastic surface am...  \n","9091  1. 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A paper feeder, comprising: a housing; a dr...  \n","\n","[4888 rows x 4 columns]"],"text/html":["\n","  <div id=\"df-a77e1a79-09be-4e22-9b3c-8b1dbcc0b92d\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>patent_number</th>\n","      <th>decision</th>\n","      <th>abstract</th>\n","      <th>claims</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>13144833</td>\n","      <td>0</td>\n","      <td>Regimen for the treatment of rosacea include t...</td>\n","      <td>1. 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The fusion prot...   \n","16150      15330955      0  A pipe extraction tool that grips the inside o...   \n","\n","                                                  claims  \n","0      1. A compact optical network terminal, compris...  \n","1      1. A method comprising: using a first reader t...  \n","3      1. A crystal growth furnace for growing a crys...  \n","4      1. A sole member of footwear comprising a base...  \n","5      1. A ratchet tool, comprising a shaft member, ...  \n","...                                                  ...  \n","16144  1. A wavelength tunable laser device, comprisi...  \n","16145  1. (canceled) 2. The method of claim 19, where...  \n","16148  1. A controlling method of a robot hand, the r...  \n","16149  1. A fusion protein comprising an Fc fragment ...  \n","16150  1. 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A ratchet tool, comprising a shaft member, ...</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>16144</th>\n","      <td>15002390</td>\n","      <td>1</td>\n","      <td>A wavelength tunable laser device, including: ...</td>\n","      <td>1. A wavelength tunable laser device, comprisi...</td>\n","    </tr>\n","    <tr>\n","      <th>16145</th>\n","      <td>15002391</td>\n","      <td>1</td>\n","      <td>In one aspect, a method for use in preparing a...</td>\n","      <td>1. (canceled) 2. The method of claim 19, where...</td>\n","    </tr>\n","    <tr>\n","      <th>16148</th>\n","      <td>15002394</td>\n","      <td>1</td>\n","      <td>A robot hand controlling method executes calcu...</td>\n","      <td>1. 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This time, we will just go with `distilbert-base-uncased` for simplicity."]},{"cell_type":"markdown","source":["### Initializing Classes and Trainers"],"metadata":{"id":"YklaXlgDO6Jw"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hxE_CIT_dTzH","executionInfo":{"status":"ok","timestamp":1682021471720,"user_tz":240,"elapsed":16542,"user":{"displayName":"Ryan Kim","userId":"18356277368138721144"}},"outputId":"758b0092-d56e-47b6-852a-4a19915bfe0c"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: torch in /usr/local/lib/python3.9/dist-packages (2.0.0+cu118)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.9/dist-packages (from torch) (3.1.2)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.9/dist-packages (from torch) 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/usr/local/lib/python3.9/dist-packages (from requests->transformers) (3.4)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2022.12.7)\n","Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (1.26.15)\n","Installing collected packages: tokenizers, transformers\n","Successfully installed tokenizers-0.13.3 transformers-4.28.1\n"]}],"source":["!pip install torch\n","!pip install transformers"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"i8_0Ih_WdTzH"},"outputs":[],"source":["from torch.utils.data import Dataset, DataLoader\n","from transformers import DistilBertTokenizerFast, DistilBertForSequenceClassification\n","from transformers import Trainer, TrainingArguments, AdamW"]},{"cell_type":"code","source":["torch.backends.cuda.matmul.allow_tf32 = True\n","model_name = \"distilbert-base-uncased\"\n","upsto_abstracts_model_path = './models/uspto_abstracts'\n","upsto_claims_model_path = './models/uspto_claims'"],"metadata":{"id":"wXkvS5h2NrzW","executionInfo":{"status":"ok","timestamp":1682032710087,"user_tz":240,"elapsed":217,"user":{"displayName":"Ryan Kim","userId":"18356277368138721144"}}},"execution_count":39,"outputs":[]},{"cell_type":"markdown","source":["We will create a Dataset class for the training"],"metadata":{"id":"awXD1_ltNxPC"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"yVi-Vhb-dTzH"},"outputs":[],"source":["class USPTODataset(Dataset):\n","    def __init__(self, encodings, labels):\n","        self.encodings = encodings\n","        self.labels = labels\n","    def __getitem__(self, idx):\n","        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}\n","        item['labels'] = torch.tensor(self.labels[idx])\n","        return item\n","    def __len__(self):\n","        return len(self.labels)\n"]},{"cell_type":"markdown","source":["### Double-Checking the Data\n","\n","We will do a basic check: Do we have `trainData` and `valData` cached? If not, we need to load it in!"],"metadata":{"id":"ZXqCGaTxN7qy"}},{"cell_type":"code","source":["trainDataPath = \"./data/train.json\"\n","valDataPath = \"./data/val.json\"\n","\n","if trainData is None and os.path.exists(trainDataPath):\n","  f = open(trainDataPath)\n","  trainData = json.load(f)\n","  f.close()\n","if valData is None and os.path.exists(valDataPath):\n","  f = open(valDataPath)\n","  valData = json.load(f)\n","  f.close()"],"metadata":{"id":"8Szn0TJ-N7CI"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Encoding the Data"],"metadata":{"id":"V3oKe81RPIgq"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"4Cxzzr6KdTzI","executionInfo":{"status":"ok","timestamp":1682021490811,"user_tz":240,"elapsed":1763,"user":{"displayName":"Ryan Kim","userId":"18356277368138721144"}},"colab":{"base_uri":"https://localhost:8080/","height":257,"referenced_widgets":["37e352aeab994637887c9fce16a4fdda","7ce12e08913445429c0f44752b5f821c","6999d35a0c09459b9d0c9d47dba70320","5dc74e126ba4481e8e15ffa59b1eaf8e","3d9ca529621f46da9ed93641ae56b4ea","d9e649a7a52641b28b77037fc4713d77","3d83814aa933459dac4d493ab6c2ecf7","a08f7de9b7284616a3a6f2176804a714","2f624544ba68401491be11bb78cc8086","e7d6e1e3fb9a49e0b58281aca52517b5","51b1280a3a5e4facbeafb28923d77133","0da194d892754092ad01803ff69c9a7c","1e085de4a50e4c2685b9d24e0f289679","373fdb0d94684d44aa5e0e6293319bc6","c31e597bc5c14d14b287206ea8be2522","acf120e8d7f14a23a7a8a8f6d2c72d54","8bbb84dc028a4b62b1ea4dcd98131706","72125209dca54decaae05e5678a9eb60","659e0520847d4db5a5cf717a7be903b1","7077cb21a4b9491ab20b2af5dd7d30e5","517fdc4c1e61453f9e167dd8cc33f021","055f263ecfab430da77808fdc07699a1","cb4f082d2c384b74a54bac7e92b19772","12f8fa71da0d434a88c43ab13159fbc6","cb2d86cc73fd4a529d75aeb8e9c354ae","28bff5766c51461e8b9456c07aac9c57","8f3f4ca0a7114fb3929b2b80402c19ad","097daf3ff77f4d39809fe3a9d5bbd3c3","e53f41626ff34cbca574ef5be6b910e9","e7a1f0216c184d5e8abee0f4998f7cb7","ec7f6f10a68f4aa3b1696e4e1d59c231","041087211da7424e86b03574c00bcc7e","44c305d3e3ec44a1ac31a9e82ee00fd5","6cce9c60a7074c40ad9992597eb1f50a","87cad6102054466d8e1243da205cf506","5d1ae7f7479e485a97e80db391b6e694","789cf158a3154bba8b1091b2ec443843","f4d8392b478149949a77bf606fea3090","d457e5284b6e4ecf8efddff65b613315","abff237c84fe446f857de2c7c6fc466c","e34a8a0a27614aab95e63b221861965f","bf607dd1b0ba47c2a4b42cd934786356","f66a864297f1446d92968786100fa6ef","035fd49261424e179b16f2ae4688944e"]},"outputId":"d1afd722-6591-4860-db86-5bb9ffd58e7d"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"37e352aeab994637887c9fce16a4fdda"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0da194d892754092ad01803ff69c9a7c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)/main/tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cb4f082d2c384b74a54bac7e92b19772"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)lve/main/config.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6cce9c60a7074c40ad9992597eb1f50a"}},"metadata":{}}],"source":["# Initializing the Tokenizer\n","tokenizer = DistilBertTokenizerFast.from_pretrained(model_name)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"suHHAY90dTzI"},"outputs":[],"source":["# Encoding the Data\n","train_abstracts_encodings = tokenizer(trainData[\"abstracts\"], truncation=True, padding=True)\n","train_claims_encodings = tokenizer(trainData[\"claims\"], truncation=True, padding=True)"]},{"cell_type":"code","source":["# Creating the Datasets from the data\n","train_abstracts_dataset = USPTODataset(train_abstracts_encodings, trainData[\"labels\"])\n","train_claims_dataset = USPTODataset(train_claims_encodings, trainData[\"labels\"])"],"metadata":{"id":"hptmNAJ1PcZN"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Model Preparation\n","\n","We need to initialize the model that we will use as a base now."],"metadata":{"id":"22RFJcEXPnZB"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":830,"referenced_widgets":["a7752b9c0c89474ab6662cbc5a19c513","2f392aea78f945d2ac8198a9be17288b","25c8b6c20fd84746a0b3b20a99d1b5bf","f0b3815c4e314899aba7256040f4cbbb","75d63216400645deab0d6e56cea0a67c","a03b5a76f061429aba6fc0935fbb46c8","8aaea1deb9ea452798858d0209668bda","a1c2445dd87f4426ba565b7483edca24","47943fe3054f42e3856dfd9d2d7b362a","e6531320c3fa445baa68aad6c20f6388","da050369b5e8464089e69a326f34fa43"]},"id":"_TuzDNWsdTzI","executionInfo":{"status":"ok","timestamp":1682021554233,"user_tz":240,"elapsed":15686,"user":{"displayName":"Ryan Kim","userId":"18356277368138721144"}},"outputId":"862da1d9-10c5-4aa8-d241-8edcb6d27b21"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading pytorch_model.bin:   0%|          | 0.00/268M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a7752b9c0c89474ab6662cbc5a19c513"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_projector.weight', 'vocab_layer_norm.weight', 'vocab_projector.bias', 'vocab_transform.weight', 'vocab_layer_norm.bias', 'vocab_transform.bias']\n","- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.bias', 'classifier.bias', 'classifier.weight', 'pre_classifier.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]},{"output_type":"execute_result","data":{"text/plain":["DistilBertForSequenceClassification(\n","  (distilbert): DistilBertModel(\n","    (embeddings): Embeddings(\n","      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n","      (position_embeddings): Embedding(512, 768)\n","      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","      (dropout): Dropout(p=0.1, inplace=False)\n","    )\n","    (transformer): Transformer(\n","      (layer): ModuleList(\n","        (0-5): 6 x TransformerBlock(\n","          (attention): MultiHeadSelfAttention(\n","            (dropout): Dropout(p=0.1, inplace=False)\n","            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n","            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n","            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n","            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n","          )\n","          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","          (ffn): FFN(\n","            (dropout): Dropout(p=0.1, inplace=False)\n","            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n","            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n","            (activation): GELUActivation()\n","          )\n","          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","        )\n","      )\n","    )\n","  )\n","  (pre_classifier): Linear(in_features=768, out_features=768, bias=True)\n","  (classifier): Linear(in_features=768, out_features=2, bias=True)\n","  (dropout): Dropout(p=0.2, inplace=False)\n",")"]},"metadata":{},"execution_count":27}],"source":["device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\n","model = DistilBertForSequenceClassification.from_pretrained(model_name)\n","model.to(device)\n","model.train()"]},{"cell_type":"markdown","source":["### Training Preparation"],"metadata":{"id":"ff23ZnIMQOPj"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"nQJhJGNmdTzI"},"outputs":[],"source":["train_abstracts_loader = DataLoader(train_abstracts_dataset, batch_size=32, shuffle=True)\n","train_claims_loader = DataLoader(train_claims_dataset, batch_size=32, shuffle=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jgFY2KNSdTzJ"},"outputs":[],"source":["optim = AdamW(model.parameters(), lr=5e-5)"]},{"cell_type":"markdown","source":["### Training!\n","\n","We will be training for 10 epochs"],"metadata":{"id":"jSfVwiFZQfyF"}},{"cell_type":"code","source":["def Train(loader, save_path, num_train_epochs=2):\n","  batch_num = len(loader)\n","  for epoch in range(num_train_epochs):\n","    print(f'\\t- Training epoch {epoch+1}/{num_train_epochs}')\n","    batch_count = 0\n","    for batch in loader:\n","      print(f'{batch_count}|{batch_num} - {round((batch_count/batch_num)*100)}%', end=\"\")\n","      #print('\\t\\t- optim zero grad')\n","      optim.zero_grad()\n","      #print('\\t\\t- input_ids')\n","      input_ids = batch['input_ids'].to(device)\n","      #print('\\t\\t- attention_mask')\n","      attention_mask = batch['attention_mask'].to(device)\n","      #print('\\t\\t- labels0')\n","      labels = batch['labels'].to(device)\n","      #print('\\t\\t- outputs')\n","      outputs = model(input_ids, attention_mask=attention_mask, labels=labels)\n","            \n","      #print('\\t\\t- loss')\n","      loss = outputs[0]\n","      #print('\\t\\t- backwards')\n","      loss.backward()\n","      #print('\\t\\t- step')\n","      optim.step()\n","\n","      batch_count += 1\n","      print(\"\\r\", end=\"\")\n","    \n","    model.save_pretrained(save_path, from_pt=True) \n","    print(f'Saved model in {save_path}!\\n')"],"metadata":{"id":"vrCBIFTOQoqH"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6o-434bzdTzJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1682030797217,"user_tz":240,"elapsed":9174487,"user":{"displayName":"Ryan Kim","userId":"18356277368138721144"}},"outputId":"b02fca82-05a2-48d7-9a85-5f4521cfb1b4"},"outputs":[{"output_type":"stream","name":"stdout","text":["=== TRAINING ABSTRACTS ===\n","\t- Training epoch 1/10\n","Saved model in ./models/upsto_abstracts!\n","\n","\t- Training epoch 2/10\n","Saved model in ./models/upsto_abstracts!\n","\n","\t- Training epoch 3/10\n","Saved model in ./models/upsto_abstracts!\n","\n","\t- Training epoch 4/10\n","Saved model in ./models/upsto_abstracts!\n","\n","\t- Training epoch 5/10\n","Saved model in ./models/upsto_abstracts!\n","\n","\t- Training epoch 6/10\n","Saved model in ./models/upsto_abstracts!\n","\n","\t- Training epoch 7/10\n","Saved model in ./models/upsto_abstracts!\n","\n","\t- Training epoch 8/10\n","Saved model in ./models/upsto_abstracts!\n","\n","\t- Training epoch 9/10\n","Saved model in ./models/upsto_abstracts!\n","\n","\t- Training epoch 10/10\n","Saved model in ./models/upsto_abstracts!\n","\n","----\n","=== TRAINING CLAIMS ===\n","\t- Training epoch 1/10\n","Saved model in ./models/upsto_claims!\n","\n","\t- Training epoch 2/10\n","Saved model in ./models/upsto_claims!\n","\n","\t- Training epoch 3/10\n","Saved model in ./models/upsto_claims!\n","\n","\t- Training epoch 4/10\n","Saved model in ./models/upsto_claims!\n","\n","\t- Training epoch 5/10\n","Saved model in ./models/upsto_claims!\n","\n","\t- Training epoch 6/10\n","Saved model in ./models/upsto_claims!\n","\n","\t- Training epoch 7/10\n","Saved model in ./models/upsto_claims!\n","\n","\t- Training epoch 8/10\n","Saved model in ./models/upsto_claims!\n","\n","\t- Training epoch 9/10\n","Saved model in ./models/upsto_claims!\n","\n","\t- Training epoch 10/10\n","Saved model in ./models/upsto_claims!\n","\n"]}],"source":["print(\"=== TRAINING ABSTRACTS ===\")\n","Train(train_abstracts_loader,upsto_abstracts_model_path, num_train_epochs=10)\n","print(\"----\")\n","print(\"=== TRAINING CLAIMS ===\")\n","Train(train_claims_loader,upsto_claims_model_path, num_train_epochs=10)"]},{"cell_type":"code","execution_count":40,"metadata":{"id":"IUIwKTDVdTzJ","colab":{"base_uri":"https://localhost:8080/","height":35},"executionInfo":{"status":"ok","timestamp":1682032764012,"user_tz":240,"elapsed":30306,"user":{"displayName":"Ryan Kim","userId":"18356277368138721144"}},"outputId":"70ca9022-a039-4d35-a7f1-4e1c570af2c1"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'/content/uspto_claims.zip'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":40}],"source":["import shutil\n","shutil.make_archive(\"uspto_abstracts\", 'zip', './models/uspto_abstracts')\n","shutil.make_archive(\"uspto_claims\", 'zip', './models/uspto_claims')\n"]},{"cell_type":"code","source":[],"metadata":{"id":"c0P0PK7V9BDA"},"execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 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