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in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (1.9.2)\n", + "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (4.0.2)\n", + "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (23.1.0)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->evaluate) (1.16.0)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Training the Model\n", + "## Loading the Data\n", + "To train the model, first I loaded the HUPD dataset sample, which consists of all patent applications from Jan 2016. The sample of the dataset contains 25,247 examples, 16153 (64%) for training and 9094 (46%) for validation.\n", + "\n", + "## Choosing DistilBERT\n", + "I tested out several models during training. I was initially interested in using Longformer and Big Bird, which support longer input sequences. However, in order to run them with full dataset, the models could only take small batch sizes (n<=4) without running out of GPU RAM and generally would've taken too to run repeatedly. I also tried BERT, but found it didn't offer a susbtantial increase in accuracy by the end of the training process, so I stuck with distilBERT for its balance of performance and speed. 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