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"f1ee39d426a34849934cd70f392e60d4", "8249ee89d7e444b0a4c99b9550640b18", "e4e5cdf7720349c29bfeb2d15b3018ef", "3bdb44bd5c6a44aaa38e798d37be4ed4", "72a3d60d81094a98af992ad9cfc454de", "944f64a6c0ac4845b1b15e4591e664d2", "062c2971c6f5486d891c5e09a9476cc7", "206a3559f4f14d7bb8dfb7b8a5444a69", "d696025f4033419ea4c2d143a5911f15", "434a81015e7e4b038ae3b880b00ad080", "062f3eaff5874d738093a0e34c58f91c", "5ee32610b9c04aabaecb1ca478218468", "84f3b8d7fc8a4ac59d68e25ff217cb76", "247e77c84b264daaa54ca32dec6c99b0", "9c434f2aad8a407eb8df8e7a9478f01b", "d7d6596638d944d7a33d91b7da3bd7b8", "36eaf60be43642059f6f78805424ebfe", "75b6ede503e8493fa3084c7693e84a8d", "2cdbe44708db43cab03c8c73bb8b363f", "9c97850dc39143e6b5f1f4f820f5209b", "3e2f37b36cc943f4825888166b0cff73", "5534432d81c74787a9fadf6b93cd3c66", "466ec272c864446c94c2280e968fad74", "7b23c028d99d4e87a80b278e60133456", "bbf5dbafda8b483cbf03d7557e43563d", "c8c6836d42d24a4784c10f98e9804f52", "e674bca2be874fd097da0bed713a2b19", "33eabd3623b741b882b6f01af6f14fb8", "42cf0b6774924cc591f88e64ef8b95c7" ] }, "id": "kwCRJqewX0d_", "outputId": "a2c7a057-27a9-4c52-dc89-4dd9859fc7ae" }, "execution_count": 3, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Downloading builder script: 0%| | 0.00/1.53k [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/setfit/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, trial)\u001b[0m\n\u001b[1;32m 280\u001b[0m \u001b[0moptimizer_params\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"lr\"\u001b[0m\u001b[0;34m:\u001b[0m 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show_progress_bar, checkpoint_path, checkpoint_save_steps, checkpoint_save_total_limit)\u001b[0m\n\u001b[1;32m 720\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 721\u001b[0m \u001b[0mloss_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloss_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 722\u001b[0;31m \u001b[0mloss_value\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 723\u001b[0m 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177\u001b[0m def grad(\n", "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ] }, { "cell_type": "code", "source": [ "metrics = trainer.evaluate()\n", "metrics" ], "metadata": { "id": "VkOPAvBnYMAO" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "acc_setfit=[]\n", "acc_setfit=acc_setfit.append(metrics['accuracy'])" ], "metadata": { "id": "o9mNeXytZ5tV" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "n_samples=[]\n", "n_samples=n_samples.append(num_samples)" ], "metadata": { "id": "JVdsrxP7aUXt" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "preds = model([\"i loved the spiderman movie!\", \"pineapple on pizza is the worst 🤮\"])\n", "preds" ], "metadata": { "id": "7iWzkI1HYOa2" }, "execution_count": null, "outputs": [] } ] }