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"source": [ "!pip install datasets" ] }, { "cell_type": "code", "source": [ "!pip install transformers" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "d3q7HTmjM1XQ", "outputId": "24c42fd7-681c-4665-c073-027eb81269f7" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting transformers\n", " Downloading transformers-4.15.0-py3-none-any.whl (3.4 MB)\n", "\u001b[K |████████████████████████████████| 3.4 MB 6.8 MB/s \n", "\u001b[?25hCollecting tokenizers<0.11,>=0.10.1\n", " Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n", "\u001b[K |████████████████████████████████| 3.3 MB 45.7 MB/s \n", "\u001b[?25hRequirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.2.1)\n", "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages 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895 kB 60.6 MB/s \n", "\u001b[?25hRequirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.8.2)\n", "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (3.10.0.2)\n", "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.6)\n", "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.6.0)\n", "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.10.8)\n", "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n", "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n", "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.1.0)\n", "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n", "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n", "Installing collected packages: pyyaml, tokenizers, sacremoses, transformers\n", " Attempting uninstall: pyyaml\n", " Found existing installation: PyYAML 3.13\n", " Uninstalling PyYAML-3.13:\n", " Successfully uninstalled PyYAML-3.13\n", "Successfully installed pyyaml-6.0 sacremoses-0.0.46 tokenizers-0.10.3 transformers-4.15.0\n" ] } ] }, { "cell_type": "code", "source": [ "!wget \"https://archive.ics.uci.edu/ml/machine-learning-databases/00462/drugsCom_raw.zip\"" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "lwino22W7fyT", "outputId": "76d00137-ad25-4748-c7f5-40244220092f" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--2021-12-26 14:04:06-- https://archive.ics.uci.edu/ml/machine-learning-databases/00462/drugsCom_raw.zip\n", "Resolving archive.ics.uci.edu (archive.ics.uci.edu)... 128.195.10.252\n", "Connecting to archive.ics.uci.edu (archive.ics.uci.edu)|128.195.10.252|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 42989872 (41M) [application/x-httpd-php]\n", "Saving to: ‘drugsCom_raw.zip’\n", "\n", "drugsCom_raw.zip 100%[===================>] 41.00M 89.9MB/s in 0.5s \n", "\n", "2021-12-26 14:04:07 (89.9 MB/s) - ‘drugsCom_raw.zip’ saved [42989872/42989872]\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "!unzip drugsCom_raw.zip" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "l_-BJiC-7rQV", "outputId": "bdab5aaf-b1b7-4ffa-9e8b-7eb3842c5072" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Archive: drugsCom_raw.zip\n", " inflating: drugsComTest_raw.tsv \n", " inflating: drugsComTrain_raw.tsv \n" ] } ] }, { "cell_type": "code", "source": [ "from datasets import load_dataset\n", "data_files = {\"train\": \"drugsComTrain_raw.tsv\", \"test\": \"drugsComTest_raw.tsv\"}\n", "drug_dataset = load_dataset(\"csv\", data_files=data_files, delimiter=\"\\t\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 185, "referenced_widgets": [ "281593e9e8964ec0a32bef55b9deb148", "5f0d72f242b44bc89754b704a522b08d", "cae22167d54c49a4a8f9ff94e8dd01c4", "424971c141754da8ac684fe5ce82ca4e", "49bc85aa3b7c407b943bcea36d80d580", "281c6fe1deef4d0d87b242c045070af3", "97df58e119544ff9b3f78fe0ef94338c", "b37b24bd9ea64a06868686c9510dc42e", "e7c1ff922d8a48dcb55f0f052e0b2b86", "0f9dc4643ba245119c2856250ee0d021", "26a49bd5cf64468c8dd324e4d91287dd", "8e47e67ad1ac4234aa5fdef4061a21e9", "d6b655148d244d8a9039fd5673bf3337", "46962589203c460ab7936b757440928d", "392a948a2af5411db0ebb9075e7f6227", "6a950ddd18d04ce19874f9c8749973d2", "50893c85dc614ca0bb31b465e4a2def4", "e00fb5fe5f3e4b8f8541282aba8b38c3", "b1aaf6d7c49446879e1481415c61f9a8", "41e05be2c32743d1978442a33a1cb8ba", "c9ecc03947144c928e13644d62374390", "06ad456028e1450b8bea25886dd71e5c", "0af29e5651934d76baf4d1787889d198", "0a30f87297ab4a7bb81ad180c0226454", "494e72dc0a4d4abbbf758072d5667a8b", "31b94f12c77c4dc381e2e65d7fa38fa2", "7bf474933cdb47c296c3c98c4de8c19a", "1575f71559f541d7afea3233e6fe4586", "402e764b7a8749e79fe9e48f0bbb5763", "f60f3d4dd5f94a2f84088f0c7ffb469c", "3a8b24fa12dc4d6c80f681042a5cfc2a", "77e9801f54db4a539402c1ddc08f577f", "f462fd77083f4c8684043e47465b5867" ] }, "id": "MIhgoeS47yGQ", "outputId": "1da4bd07-9f7d-4762-c471-bf21070d71db" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Using custom data configuration default-3761173c276c0a9a\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Downloading and preparing dataset csv/default to /root/.cache/huggingface/datasets/csv/default-3761173c276c0a9a/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e...\n" ] }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "281593e9e8964ec0a32bef55b9deb148", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/2 [00:00 30)\n", "print(drug_dataset.num_rows)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 98, "referenced_widgets": [ "19972ac7a9524fae88bec023d5624134", "bcb77810547a49d784d8c8987055857f", "47ea578ad8e6466b9ed354e382d8c060", "1a16cbf23aea441ba8afa1e1b40562bb", "67b0d0d7407a450ea786fc685d3d20c9", "b9afc23a3cc7467abb9863b0dde54f68", "77e0cf1cc69843c98529fb2e90ac34d1", "c38efe098a7346aa93ffa8e629f4de23", "681b924c19944c3a948de88f05cb028b", "860495d9fa444c7a8c4d411aad233865", "5a15918a9e544ac586f1798602cfff8d", "b2e8cd19f16843a6a85c23cc607bccf3", "ec7b9dd6548e41b39a0cec8bee978ad6", "45155064990a4a3ba6a357be809427d6", "4aa1c46e33fd46dba8af7cf0bb5d3994", "ff8a6ab06c2c4669ade31e65adf6a6f2", "4ebacf83a7094147b16b8b4120e56e9d", "5a2667b5f4424819b1e23889697d3299", "cc022b7d144749de93fbfed810aa6c97", "8c9ed4f2370d4deaaf5f54ede278f2f6", "2587b7d3a95a49eb8ac86a2abaf38f7b", "8ecd319a00bf472eb67b4f2065876f66" ] }, "id": "GS2CTTiZFyMh", "outputId": "a3f6e7f1-c758-4553-bbf4-06db0fadf09d" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "19972ac7a9524fae88bec023d5624134", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/161 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtokenized_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdrug_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenize_and_split\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatched\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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/datasets/dataset_dict.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, function, with_indices, input_columns, batched, batch_size, remove_columns, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, desc)\u001b[0m\n\u001b[1;32m 510\u001b[0m \u001b[0mdesc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdesc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 511\u001b[0m )\n\u001b[0;32m--> 512\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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 513\u001b[0m }\n\u001b[1;32m 514\u001b[0m )\n", "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/datasets/dataset_dict.py\u001b[0m in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 510\u001b[0m \u001b[0mdesc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdesc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 511\u001b[0m )\n\u001b[0;32m--> 512\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\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 513\u001b[0m }\n\u001b[1;32m 514\u001b[0m )\n", "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\u001b[0m\n\u001b[1;32m 2108\u001b[0m \u001b[0mnew_fingerprint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnew_fingerprint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2109\u001b[0m \u001b[0mdisable_tqdm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdisable_tqdm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2110\u001b[0;31m \u001b[0mdesc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdesc\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 2111\u001b[0m )\n\u001b[1;32m 2112\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[0;32m/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 516\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"Dataset\"\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"self\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 517\u001b[0m \u001b[0;31m# apply actual function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 518\u001b[0;31m \u001b[0mout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"DatasetDict\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 519\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 520\u001b[0m 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"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/datasets/fingerprint.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 409\u001b[0m \u001b[0;31m# Call actual function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 411\u001b[0;31m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 413\u001b[0m \u001b[0;31m# Update fingerprint of in-place transforms + update in-place history of transforms\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36m_map_single\u001b[0;34m(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset, disable_tqdm, desc, cache_only)\u001b[0m\n\u001b[1;32m 2484\u001b[0m \u001b[0mwriter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2485\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[0;32m-> 2486\u001b[0;31m 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"55765c91f69a434bbeee0ec4c958ec4a", "124c93b98e4849d5a6ffae08778c30af", "90396fc7a5304bf5b9e40079bf01fcca", "a23c1206f8bb4e8d94e7124c8876aabb", "e421f3c846f84af59fec6d0df18165ca", "82752376f32b43068618d4c6e171b4b6", "d15b58a1ebdc4a99ae5f17dadd2b049b", "48409e890cc043989cba4e0cde911244", "b8c4dee5841246deb177038bd2a6b164", "ed88ac6ac20f4abea6b1a6fafe431278", "782b9b92a8634fe3bb1a6b5275f19cff" ] }, "id": "QNmSMvN0VYx9", "outputId": "53ec8ab0-0b54-4370-aeaa-d37f28d33240" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "832da29a59414c4e96a6a84209b63b02", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/139 [00:00\n", "
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patient_iddrugNameconditionreviewratingdateusefulCountreview_length
095260Guanfacineadhd\"My son is halfway through his fourth week of ...8.0April 27, 2010192141
192703Lybrelbirth control\"I used to take another oral contraceptive, wh...5.0December 14, 200917134
2138000Ortho Evrabirth control\"This is my first time using any form of birth...8.0November 3, 20151089
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conditionfrequency
0birth control27655
1depression8023
2acne5209
3anxiety4991
4pain4744
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drugNamerating
2060OxyContin8.586667
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\n", " " ], "text/plain": [ " drugName rating\n", "2060 OxyContin 8.586667" ] }, "metadata": {}, "execution_count": 70 } ] }, { "cell_type": "code", "source": [ "drug_dataset.reset_format()" ], "metadata": { "id": "_edYqGulOFGW" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "drug_dataset_clean = drug_dataset[\"train\"].train_test_split(train_size=0.8, seed=42)\n", "drug_dataset_clean[\"validation\"] = drug_dataset_clean.pop(\"test\")\n", "drug_dataset_clean[\"test\"] = drug_dataset[\"test\"]\n", "drug_dataset_clean" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "CXbbRHRhPfvD", "outputId": "b2202b07-48c8-4893-cd65-600d43934b1f" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Loading cached split indices for dataset at /root/.cache/huggingface/datasets/csv/default-3761173c276c0a9a/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e/cache-182ef4736d384abd.arrow and /root/.cache/huggingface/datasets/csv/default-3761173c276c0a9a/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e/cache-debcedfd6a32499a.arrow\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "DatasetDict({\n", " train: Dataset({\n", " features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n", " num_rows: 110811\n", " })\n", " validation: Dataset({\n", " features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n", " num_rows: 27703\n", " })\n", " test: Dataset({\n", " features: ['patient_id', 'drugName', 'condition', 'review', 'rating', 'date', 'usefulCount', 'review_length'],\n", " num_rows: 46108\n", " })\n", "})" ] }, "metadata": {}, "execution_count": 81 } ] }, { "cell_type": "code", "source": [ "drug_dataset_clean.save_to_disk(\"drug_reviews\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81, "referenced_widgets": [ "b5a34cbe2cd942c3867f70b880eaec52", "fdcd0dd845244debb0421558c01143c9", "26eb07fddf604af2bf4b5e0f3b833d77", "fe9b6afb8bcc4cf5bdf4ca9108516bb2", "07978b9d0b344bebb081a684956e6c2e", "afbd8f7e8bde4425b08646d36942595e", "2b6348b223c04f97b36e9faa4561ad2b", "3bf9dc19420441f195b7be335214c0e4", "8a80497864db4d2fbebcdb6ef1495ad2", "d485d46b139f47d3af6573ba69c5d6c3", "f41cc85ff60f494da53efdcb6153be61", "a8b786a21777429284684413989c84a7", "8197d978edc44c2389a0dedac054f02e", "abff636ad0c74ccfb5e8b626bb9e9822", "10fe3c970f6a4e41a13126be433d019c", "c26e75aaf473465fbb37238d52f6ec26", "8ec16acfc7ad42be85907a2619aa335e", "0191473df6664fe3bd25b5bcd9d1e2fa", "27aa40c90b314b76ac60f9caaef3dc1d", "cf85e34f2625470c902f5e8516d40fec", "5af4486076e247eba2443641b19061f5", "b87fcc903ced4949bfbac77b826bf47b" ] }, "id": "3Aer00sgSm5V", "outputId": "de60d7c2-7da5-479b-f833-0d9d5dba6da9" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b5a34cbe2cd942c3867f70b880eaec52", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Flattening the indices: 0%| | 0/111 [00:00" ] }, "metadata": {}, "execution_count": 117 } ] }, { "cell_type": "code", "source": [ "tokenized_dataset = pubmed_dataset_streamed.map(lambda x: tokenizer(x[\"review\"]), batched=True)\n", "next(iter(tokenized_dataset))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 200 }, "id": "AxS8tNj_wnUV", "outputId": "3c5bcd24-b262-4248-a239-67fe381dc431" }, "execution_count": null, "outputs": [ { "output_type": "error", "ename": "TypeError", "evalue": "ignored", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mtokenized_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpubmed_dataset_streamed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"review\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatched\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtokenized_dataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: 'IterableDataset' object is not an iterator" ] } ] }, { "cell_type": "code", "source": [ "shuffled_dataset = pubmed_dataset_streamed.shuffle(buffer_size=1000,seed=42)" ], "metadata": { "id": "Zqe95BHwxXvy" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "train_dataset = shuffled_dataset.skip(500)\n", "validation_dataset = shuffled_dataset.take(500)" ], "metadata": { "id": "zsGnBEWC1ZyZ" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "pubmed_dataset_streamed2 = load_dataset(\n", " \"csv\", data_files=\"drugsComTest_raw.tsv\", split=\"train\", delimiter=\"\\t\", streaming=True\n", ")\n", "next(iter(pubmed_dataset_streamed2))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_LZd827S2bA5", "outputId": "f7333dfa-f5a3-46d6-961f-01046521af55" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Using custom data configuration default-f7dc8c58638ca9ea\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "{'Unnamed: 0': 163740,\n", " 'condition': 'Depression',\n", " 'date': 'February 28, 2012',\n", " 'drugName': 'Mirtazapine',\n", " 'rating': 10.0,\n", " 'review': '\"I've tried a few antidepressants over the years (citalopram, fluoxetine, amitriptyline), but none of those helped with my depression, insomnia & anxiety. My doctor suggested and changed me onto 45mg mirtazapine and this medicine has saved my life. Thankfully I have had no side effects especially the most common - weight gain, I've actually lost alot of weight. I still have suicidal thoughts but mirtazapine has saved me.\"',\n", " 'usefulCount': 22}" ] }, "metadata": {}, "execution_count": 149 } ] }, { "cell_type": "code", "source": [ "from itertools import islice\n", "from datasets import interleave_datasets\n", "\n", "combined_dataset = interleave_datasets([pubmed_dataset_streamed, pubmed_dataset_streamed2])\n", "list(islice(combined_dataset, 2))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "mfBA20l52e4G", "outputId": "460b0692-1377-4cb4-fb1d-df1ae577dd08" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[]" ] }, "metadata": {}, "execution_count": 151 } ] }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "yxYkiOGVNzAj" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from datasets import load_dataset\n", "sv_dataset = load_dataset(\"oscar\", \"unshuffled_deduplicated_sv\", streaming=True)\n", "sv_dataset = sv_dataset[\"train\"]\n", "next(iter(sv_dataset))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "RA3SWb0Y3V0S", "outputId": "823dd2c2-1025-4bf8-ee09-40681e3d73b7" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'id': 0,\n", " 'text': '1783 är ett viktigt årtal i den nya tidens historia. Det året slöts en fred i Paris och därmed blev de 13 brittiska kolonierna i Nordamerika självständiga, och landet USA bildades.\\nDet fanns många orsaker bakom koloniernas revolt mot England, bland annat ekonomiska orsaker. I England menade man att kolonierna enbart var till för moderlandets bästa. De skulle vara en marknad för brittiska tillverkare och att råvarorna skulle göra England rikt.\\nParlamentet (riksdagen) i London utfärdade en mängd lagar för att kontrollera handeln med kolonierna, allt till Englands fördel. Irritationen ökade då regeringen införde vissa stämpelavgifter och skyddstullar på brittiska varor bland annat te.\\nSpänningen ökade ytterligare 1763 då regeringen förbjöd nybyggarna att slå sig ned väster om bergskedjan Appalacherna. Avsikten var att hindra indianska oroligheter. Protester och uppror blev följden. Kolonisterna vägrade att acceptera tullar, skatter och stämpelavgifter eftersom de inte fick skicka representanter till det engelska parlamentet.\\n”Bostonmassakern” började med ett gräl mellan brittiska soldater och kolonister utanför tullhuset i Boston och slutade med att fem kolonister sköts till döds.\\n1773 inträffade det berömda ”Boston Tea Party”. Kolonister utklädda till indianer klättrade upp på tre brittiska skepp och kastade te för 25 000 pund i vattnet. Brittiska parlamentet svarade med att stänga Bostons hamn.\\nDe första skotten föll i Lexington i april 1775 och det blev upptakten till frihetskriget. Kolonisterna organiserade en arme som leddes av slavägaren George Washington. England satte in 20 000 tyska legosoldater i kriget.\\nDen 4 juli 1776 utfärdades den berömda oavhängighetsförklaringen i Philadelphia. Den mest berömda satsen i förklaringen är den att alla människor skapats lika och har samma grundläggande rättigheter, bl.a. rätt till liv, frihet och strävan efter lycka. Sedan dess har den 4 juli varit USA: s nationaldag.\\nEn av orsakerna till att kolonierna blev fria var att man fick hjälp av Frankrike och 1781 tvingades engelsmännen att kapitulera. Vid freden i Paris erkände England de 13 koloniernas självständighet.1789 skapades författningen, världens äldsta grundlag. Den 30 april 1789 installerades George Washington som USA:s förste president.\\nSymbolen för USA var flaggan som kallas för ”Stars and Stripes”. De 13 ursprungliga staterna var markerade i flaggan med 13 stjärnor i en ring och 13 ränder, omväxlande röda och vita. Det översta och understa är röda. När antalet delstater växte ökade också antalet stjärnor i flaggan. Antalet röda och vita ränder behölls oförändrat.\\n1. Beskriv några orsaker till koloniernas revolt mot England. 2. Beskriv händelserna vid Bostonmassakern.\\n7. Begreppen och de svåra orden nedan. Välj ut de tio svåraste orden. Gör ett bildspel med dessa.Visa din lärare eller förhör en kompis när du är klar.\\nrepublik, monarki, tull, skatt, avgift, stämpelavgift, parlament, grundlag, författning, politik, bojkott,\\nSkriv en sammanhållen text med egna ord. Använd gärna fler källor än minikursen. (ex youtube-filmer, so-rummet, ne.se mm.)'}" ] }, "metadata": {}, "execution_count": 6 } ] }, { "cell_type": "code", "source": [ "from transformers import AutoTokenizer\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "3d2ac27a118c4b2891d29c06d2a60bf0", "d90778ac57084654af7a6acee0749295", "847821e787f749e997c484f9bcadacfb", "2ac79de4e2eb41b285522fe8bb0cfdee", "549ddc7c442a4ff89f11b9c109ff818b", "383801eb00714653ae046c928465f1c1", "6f233114ab3b40bbb8f1ae61da5ac46e", "04cdc3cfebc6479395d4426cdaa7f9d6", "0206456f256f44f2bbb7464a52735c1b", "9fee345a2c814a3794124a4fbc3e206b", "05ffc38ad3e0487dbe5998d299e73510", "e13ead5e69624eaf9cfa1a377865a8f0", "0738ee5cd823423aba97ff2974cd793d", "5f2ab166587f45669f8f0d28d4754edf", "fb7f9fdc0c15400dae5ecfb4c4d805d5", "3fdff6191a5d486ca4b808c8d8182215", "9a2fd4424aa74613893712a8f5da1bd3", "9ce05e0122674d7392d72c60e18e1a07", "80087343733f49ebb305de4a9a35a645", "937464a3bc4845e08aabaac63b610a7f", "4880e915416c4ceaa21c8d839c5b9f8d", "527c8b3b5898466fa00c4d2cbe75621a", "a7f388981edf491eab2f9febef0d611d", "3206c67d0fd247ada65197c6846517b9", "828f5f17f1864b86b430dec18bc216b8", "9c8054c5ad254e65b4a5001816fdc679", "9433ee39e42845e1987102c5272ec32a", "388241b347dc4d1882dc8021b96e21f7", "615f248740754e2ebf4ab017b5a996f1", "1fc6bf8161aa4b8d96a57116c26b82d3", "08e0c1a591a04e5d86c3029ac023fba7", "4933a0cbe3334c149b9ba74ecf243919", "67403a0ed39747b58b01673cd16da769", "a26672d864ad44d8a7c5156e20b831cc", "bfd843e8ce5940bb8cbc66d3fb5f66a1", "4a69b9392a544907ae57f026a9d6f751", "70c2ffddfede4d5986aa8df22e2fd2c9", "9ac84f0167ad4906850ecd800beacf70", "cfd83fb2e4bb44a08b6fc19d9fbefe12", "8183ce1ea31e4ec2a9ace1329233d4b9", "18c4994acc054edeacdc06aea7b034c4", "5d7a6cc44cf14e56bf6722b8f064b42e", "0cbc0b2ce6354e3198af26012f85f1a6", "29261a49ac814a24b16606528c316bb4" ] }, "id": "XT5LAOV1MLwv", "outputId": "c095e654-38c3-47f9-838f-0fe4f6deaa54" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3d2ac27a118c4b2891d29c06d2a60bf0", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Downloading: 0%| | 0.00/28.0 [00:00 rate_limit and len(all_issues) < num_issues:\n", " all_issues.extend(batch)\n", " batch = [] # Flush batch for next time period\n", " print(f\"Reached GitHub rate limit. Sleeping for one hour ...\")\n", " time.sleep(60 * 60 + 1)\n", "\n", " all_issues.extend(batch)\n", " df = pd.DataFrame.from_records(all_issues)\n", " df.to_json(f\"{issues_path}/{repo}-issues.jsonl\", orient=\"records\", lines=True)\n", " print(\n", " f\"Downloaded all the issues for {repo}! Dataset stored at {issues_path}/{repo}-issues.jsonl\"\n", " )\n" ], "metadata": { "id": "4NSYVTaZiXs-" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "fetch_issues()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 66, "referenced_widgets": [ "1abf35d7e3f84665be946d3821684528", "d48d4dae51db421d804cb579063c8049", "1ea6e079d84446ef800bb11d1216ec15", "1df3b19b5e4a4ca893d4984097e36d33", "c853c3926d02463a85c74d6913ed7338", "16c862053a824d3c87f5171a3bafc0ef", "ef5483a5ab3c428996176b2fe48a7ab9", "e2d31c986e5b4be88c655cebaf359872", "92e840b0e057400ea43ae23dadb7e5d8", "e57aaffa10c445ae99238b0d8a253f4f", "cefe388fbd784838a975e7dda1984c94" ] }, "id": "eE9KLjNAkYn3", "outputId": "472503dc-6cd1-4eac-b05f-cec245c1648d" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1abf35d7e3f84665be946d3821684528", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/100 [00:00\n", "
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\n", " \n", " " ], "text/plain": [ " url ... is_pull_request\n", "0 https://api.github.com/repos/huggingface/datas... ... True\n", "1 https://api.github.com/repos/huggingface/datas... ... True\n", "2 https://api.github.com/repos/huggingface/datas... ... False\n", "\n", "[3 rows x 30 columns]" ] }, "metadata": {}, "execution_count": 35 } ] }, { "cell_type": "code", "source": [ "from datetime import datetime\n", "def duration_hrs(row):\n", " created = row[\"created_at\"]\n", " closed = row[\"closed_at\"]\n", " delta = closed-created\n", " hours = delta.total_seconds()/(60*60)\n", " return hours" ], "metadata": { "id": "V8_H9966mkTK" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "issues_df[\"duration\"] = issues_df.apply(lambda row: duration_hrs(row), axis=1)" ], "metadata": { "id": "PKU947xaqlki" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "issues_df[issues_df[\"duration\"].notna()][\"duration\"].mean()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "EK8exlket49h", "outputId": "fe9187fd-345d-461d-bc67-d40a64b8eecf" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "224.94468728219795" ] }, "metadata": {}, "execution_count": 67 } ] }, { "cell_type": "code", "source": [ "from datasets import Dataset\n", "issues_dataset = Dataset.from_pandas(issues_df)\n", "issues_dataset" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wctmVlzJvLb8", "outputId": "5ea14b8d-ee4c-4e72-bf45-913e399c4f0a" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Dataset({\n", " features: ['url', 'repository_url', 'labels_url', 'comments_url', 'events_url', 'html_url', 'id', 'node_id', 'number', 'title', 'user', 'labels', 'state', 'locked', 'assignee', 'assignees', 'milestone', 'comments', 'created_at', 'updated_at', 'closed_at', 'author_association', 'active_lock_reason', 'draft', 'pull_request', 'body', 'reactions', 'timeline_url', 'performed_via_github_app', 'is_pull_request', 'duration'],\n", " num_rows: 3549\n", "})" ] }, "metadata": {}, "execution_count": 70 } ] }, { "cell_type": "code", "source": [ "issue_number = 2792\n", "url = f\"https://api.github.com/repos/huggingface/datasets/issues/{issue_number}/comments\"\n", "response = requests.get(url, headers=headers)\n", "response.json()" ], "metadata": { "id": "y7unNN9mwAh1" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def get_comments(issue_number):\n", " url = f\"https://api.github.com/repos/huggingface/datasets/issues/{issue_number}/comments\"\n", " response = requests.get(url, headers=headers)\n", " return [r[\"body\"] for r in response.json()]\n", "get_comments(2792)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "uKyKc14A2sHa", "outputId": "6ffc6e3d-d568-4c29-b4d8-c4213ae09abc" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[\"@albertvillanova my tests are failing here:\\r\\n```\\r\\ndataset_name = 'gooaq'\\r\\n\\r\\n def test_load_dataset(self, dataset_name):\\r\\n configs = self.dataset_tester.load_all_configs(dataset_name, is_local=True)[:1]\\r\\n> self.dataset_tester.check_load_dataset(dataset_name, configs, is_local=True, use_local_dummy_data=True)\\r\\n\\r\\ntests/test_dataset_common.py:234: \\r\\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \\r\\ntests/test_dataset_common.py:187: in check_load_dataset\\r\\n self.parent.assertTrue(len(dataset[split]) > 0)\\r\\nE AssertionError: False is not true\\r\\n```\\r\\nWhen I try loading dataset on local machine it works fine. Any suggestions on how can I avoid this error?\",\n", " 'Thanks for the help, @albertvillanova! All tests are passing now.']" ] }, "metadata": {}, "execution_count": 72 } ] }, { "cell_type": "code", "source": [ "issues_with_comments_dataset = issues_dataset.map(\n", " lambda x: {\"comments\": get_comments(x[\"number\"])}\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "a4d3d0fa7f714e2d87a9f760ff3c5d9b", "a129ce2bfa004224b58bb65349c4d437", "0941d09fbad14fcca7e68a3f79411fbb", "0c1735142887463b825f5a7f893b3ad3", "54de99c728f244c6b3ab425a478a9007", "bdfa6f473d1948e7aa2f234c4199e2d5", "8b40c8cbf9704904abab534e46541402", "be5e8a2338694af1b0032d9b03239441", "0f9ac20005b743cea31c8a3f8dbf8a35", "5aec9265276e47088fe5dd7afb362c91", "21a5001315e8473e86b9edd2f6925b1e" ] }, "id": "zIOx9i9A3uRy", "outputId": "d7ffa78a-5d79-466e-9eec-040d34a08544" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a4d3d0fa7f714e2d87a9f760ff3c5d9b", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/3549 [00:00 main\n", "\n" ] }, { "output_type": "execute_result", "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://huggingface.co/datasets/SebastianS/github-issues/commit/44b97cc6a8ee299421dc01cd60ef25fc7681a0a5'" ] }, "metadata": {}, "execution_count": 86 } ] }, { "cell_type": "code", "source": [ "remote_dataset = load_dataset(\"SebastianS/github-issues\", split=\"train\")\n", "remote_dataset" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 255, "referenced_widgets": [ "de526ae193aa4e67aad0679f27523c83", "9f207a5a3cf74ec7bb18729eaac2772a", "533dc41fa3294a098e3b94da3894c82a", "3b7bf8411fa24d588e559ba64f122e21", "f63dc4a807b340a7b597924c8377ebe9", "c1f7893d9cf24a1498e496ee623e82f7", "1568cb82bfad4661aa6a2d05afb5db9c", "1365f8d36d694db8bf10ca6e47bc4101", "c9d2f6fcc2fc4dbe9fd46ed01ff7978d", "d690bf4d95a0499986580fb1a1c5ba8a", "5bcb369fd8d044a4892fad64f8166293", "0d8119b93e4044b0b98002bbf7cf446c", "10326274ed4842f5adab0115f2c1b714", "917b1a4021a84a8a9268849304c049f3", "4092fd50fabf4ad9b8f246279f4db4e4", "a4ef0b594210484cacb3ede355d87c34", "b3030e8a84a34a71ac7f7bd11e2083e6", "d000e81bd117442d9c61c52d0a26f442", "5cf86e57351b4207b2f4d959a966be56", "c7c64610818243e4a4c5105f1a418e4f", "436ebd6f3ee6466fb952d52b3a901272", "f2f0119990c04110b16d7803ec4c3568", "310e6ac675254873b55a721662baadc3", "43f36da19df047089c4279aedf2b93e8", "53571869a5fe45a9b1472baeca1dc3fb", "d6f7605516ea47af89d8de8a528889ea", "37e3edd6dd354c358e781ccdd94b35d2", "8a8138c4339e45bda0accc06a5f4d884", "4ebdac01b4dc4ae383f5dca86b839c12", "10fd5fe8bd9a4ec2922fe9530cdbfa97", "86ea554a77644bf5bfb2f603b73f9a06", "3c1feca13ed14aefa2cb1f0a027458e6", "50ddfb7f10e547a2ac57c6e602534caa" ] }, "id": "TH1RrW6TBJlF", "outputId": "dc729dc2-a807-4daf-cacd-e9762bb4792d" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Using custom data configuration SebastianS--github-issues-26c331b924ef95c2\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Downloading and preparing dataset json/SebastianS--github-issues to /root/.cache/huggingface/datasets/json/SebastianS--github-issues-26c331b924ef95c2/0.0.0/c90812beea906fcffe0d5e3bb9eba909a80a998b5f88e9f8acbd320aa91acfde...\n" ] }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "de526ae193aa4e67aad0679f27523c83", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/1 [00:00 main\n", "\n" ] }, { "output_type": "execute_result", "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://huggingface.co/datasets/SebastianS/github-issues/commit/bc18afe169e74ddd9c0ca76227d15bd61495b46b'" ] }, "metadata": {}, "execution_count": 90 } ] }, { "cell_type": "markdown", "source": [ "# Semantic search" ], "metadata": { "id": "_uWS4VQzeCUv" } }, { "cell_type": "code", "source": [ "!pip install transformers datasets" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "T-dJ9wDFeJIl", "outputId": "d4bc7253-3a98-4cb4-f644-1cb66dee0057" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting transformers\n", " Downloading transformers-4.15.0-py3-none-any.whl (3.4 MB)\n", "\u001b[K |████████████████████████████████| 3.4 MB 4.3 MB/s \n", "\u001b[?25hCollecting datasets\n", " Downloading datasets-1.17.0-py3-none-any.whl (306 kB)\n", "\u001b[K |████████████████████████████████| 306 kB 45.6 MB/s \n", "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.62.3)\n", "Collecting pyyaml>=5.1\n", " Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n", "\u001b[K |████████████████████████████████| 596 kB 48.3 MB/s \n", 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xxhash-2.0.2 yarl-1.7.2\n" ] } ] }, { "cell_type": "code", "source": [ "from huggingface_hub import hf_hub_url\n", "\n", "data_files = hf_hub_url(\n", " repo_id=\"lewtun/github-issues\",\n", " filename=\"datasets-issues-with-comments.jsonl\",\n", " repo_type=\"dataset\",\n", ")\n" ], "metadata": { "id": "R4574ETOHOSl" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from datasets import load_dataset\n", "\n", "issues_dataset = load_dataset(\"json\", data_files=data_files, split=\"train\")\n", "issues_dataset" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 255, "referenced_widgets": [ "03e6c647fe6c49c193e17923cd72213f", "3ec2312d248a4f0285783b494a3fb547", "4ffa37d7b0134de181481cc3936859da", "4a8f6e9610004f119ee8e564107df119", "5d43930587a84d3e85e12ee8557df8c0", "4aa1da1d8e4641c9a115a62393a3bcad", "c9fcdd6cd176466691484a17093b8d60", "e4340e98a5224118b5d42d5b988e2fc8", "7cc88f2638e540e9a6a0509440172745", "84a5d31ee22f418db723f0cfc591fb26", "0d4befa90f574b828656219120b2e02d", "9d8a6b5ea714493eac3143fd0150efc8", "b9cd08d2c5b2455097ea530a1cc7204a", "3cba410ef1ec424a8b8ae0ee74509b0b", "41eb6795c64247d9a7ab79a2cb5d4ad2", "020a656eb95d46dc99feae468307f393", "3ca413eee03b4b10a59e78671ebbb7f0", "a342114b75e04abb8c20e74f9c4e528a", "79ab509b83a34de49f0c928a09eecf8d", "1a639fcaaccd4f3eae707b5d96c663f4", "2650facb146b4e078b223c77128816fc", "85cbfc47fe414f85ab164f13201aca7c", "de6c95143f2a44e5a9eb9727d16a3c87", "d21c0488d2664abaa722fcefdf1a4351", "d936d8fd98904ac78f4565388bf60d19", "bf1799f909d246bea028544c0d10442d", "37169635be93431eb62a276564f2ae3c", "6563f9ab8a0e4bc9a6e7c1ac72e102a1", "73a623bf1076460b98666be79bdf8f56", "dac184e638bd444db50b08a20c0d25d1", "ebdeee2ecec94f67b10b9a9e4adc8354", "207d06f50d0346579474188e7bf56de0", "ef2fe559db30422990973c8f99be193c" ] }, "id": "h53H5uIDeIfM", "outputId": "09e14018-acd0-47bb-f8b7-0fc314eda1cb" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Using custom data configuration default-ece7c1527bad24e5\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Downloading and preparing dataset json/default to /root/.cache/huggingface/datasets/json/default-ece7c1527bad24e5/0.0.0/c90812beea906fcffe0d5e3bb9eba909a80a998b5f88e9f8acbd320aa91acfde...\n" ] }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "03e6c647fe6c49c193e17923cd72213f", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/1 [00:00 0)\n", ")\n", "issues_dataset" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 138, "referenced_widgets": [ "28fb2aace5f24254871e5b08e5dfee58", "7da6b95d0aaf4bfc843d265c5a105644", "bcbc95df2dbe443c86dca31423f1c831", "fa3018954eef43008c439be3e9e43f31", "b52cef6d99834adc82e2d922d671d5e5", "07292b80756a4cbfb1ad5ecf29bb1ea6", "8b4ce7eafd7c48b595896ce9deb28727", "d71b3864e5cf4675b36b7033fbd21ffc", "e1288966043645b8a0e11e9c1b50c75f", "9718ce6bacaa47feb3e63ddebff37573", "948f5cd3191644fca249520e8823dd8c" ] }, "id": "EOkBuyZae3xk", "outputId": "ee950bdf-1702-4b46-a55d-f072950c2af9" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "28fb2aace5f24254871e5b08e5dfee58", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/4 [00:00\n", "
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0https://github.com/huggingface/datasets/issues...Protect master branchCool, I think we can do both :)After accidental merge commit (91c55355b634d0d...
1https://github.com/huggingface/datasets/issues...Protect master branch@lhoestq now the 2 are implemented.\\r\\n\\r\\nPle...After accidental merge commit (91c55355b634d0d...
2https://github.com/huggingface/datasets/issues...Backwards compatibility broken for cached data...Hi ! I guess the caching mechanism should have...## Describe the bug\\r\\nAfter upgrading to data...
3https://github.com/huggingface/datasets/issues...Backwards compatibility broken for cached data...If it's easy enough to implement, then yes ple...## Describe the bug\\r\\nAfter upgrading to data...
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\n", " \n", " " ], "text/plain": [ " html_url ... body\n", "0 https://github.com/huggingface/datasets/issues... ... After accidental merge commit (91c55355b634d0d...\n", "1 https://github.com/huggingface/datasets/issues... ... After accidental merge commit (91c55355b634d0d...\n", "2 https://github.com/huggingface/datasets/issues... ... ## Describe the bug\\r\\nAfter upgrading to data...\n", "3 https://github.com/huggingface/datasets/issues... ... ## Describe the bug\\r\\nAfter upgrading to data...\n", "\n", "[4 rows x 4 columns]" ] }, "metadata": {}, "execution_count": 11 } ] }, { "cell_type": "code", "source": [ "from datasets import Dataset\n", "\n", "comments_dataset = Dataset.from_pandas(comments_df)\n", "comments_dataset" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9kRGZsXnhyhv", "outputId": "0ed5ef3b-5012-4aa1-af5a-29b0c510e004" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Dataset({\n", " features: ['html_url', 'title', 'comments', 'body'],\n", " num_rows: 2964\n", "})" ] }, "metadata": {}, "execution_count": 12 } ] }, { "cell_type": "code", "source": [ "comments_dataset = comments_dataset.map(\n", " lambda x:{\"comment_length\": len(x[\"comments\"].split())}\n", ")\n", "comments_dataset" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 118, "referenced_widgets": [ "ae37ec082a0a4e70888051606978615b", "364e2679d5074514b58fee975c4a53df", "bf268210f76f4015924f6f2df4c1b53e", "34334dcf4d5440cba4d64bba5a2de657", "66a81c96ed064c68985bc48e50276648", "8fb28ea599b04326a06f0eda4439320d", "dc9da6d556984627919e791dbcd5371c", "ed3c4e64778543e78b20f5b03e6bcdef", "62d49853f50d4a218b6f97bb737e6114", "cf541ed1ff9f43ff8eba08fade24ff7f", "142bfc19ec83403f9c63c7cca97dc04c" ] }, "id": "y4-p-Vl2iJLr", "outputId": "14def005-5b72-4ef4-832f-9dce5d14e057" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ae37ec082a0a4e70888051606978615b", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/2964 [00:00 15)\n", "comments_datase" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 118, "referenced_widgets": [ "ac519aee5cf24ab7bc43e03362ac846b", "569f131bd2be4114beb4eb8b106809fe", "918b7376183447a6944c38c33d37db97", "aa1de0f777de43c08f4b75d54c18cb7b", "fc36505e187147688363333b99a66f04", "29a9290e2ad34791b3267bf8a0b16d0b", "a33c011d408c450cae78772d031339ca", "721af05bd5984a19b607ebc99c80368e", "eb45e0703d9e402fa68a2369af63c2ad", "dd8ed3afa706430d8293b48506d52c8b", "5513066b95634a8887d4d18063ecb8c0" ] }, "id": "YaVWWff5jWBp", "outputId": "d8c55d57-735a-46a8-afb8-14065d25deb1" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ac519aee5cf24ab7bc43e03362ac846b", "version_minor": 0, "version_major": 2 }, "text/plain": [ " 0%| | 0/3 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0membeddings_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_faiss_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"embeddings\"\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/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36madd_faiss_index\u001b[0;34m(self, column, index_name, device, string_factory, metric_type, custom_index, train_size, faiss_verbose, dtype)\u001b[0m\n\u001b[1;32m 3703\u001b[0m \u001b[0mcustom_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcustom_index\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3704\u001b[0m \u001b[0mtrain_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3705\u001b[0;31m \u001b[0mfaiss_verbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfaiss_verbose\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 3706\u001b[0m )\n\u001b[1;32m 3707\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/datasets/search.py\u001b[0m in \u001b[0;36madd_faiss_index\u001b[0;34m(self, column, index_name, device, string_factory, metric_type, custom_index, train_size, faiss_verbose)\u001b[0m\n\u001b[1;32m 429\u001b[0m \u001b[0mindex_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex_name\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mindex_name\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 430\u001b[0m faiss_index = FaissIndex(\n\u001b[0;32m--> 431\u001b[0;31m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring_factory\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstring_factory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetric_type\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetric_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustom_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcustom_index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 432\u001b[0m )\n\u001b[1;32m 433\u001b[0m \u001b[0mfaiss_index\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_vectors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfaiss_verbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfaiss_verbose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/datasets/search.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, device, string_factory, metric_type, custom_index)\u001b[0m\n\u001b[1;32m 233\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_has_faiss\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 234\u001b[0m raise ImportError(\n\u001b[0;32m--> 235\u001b[0;31m \u001b[0;34m\"You must install Faiss to use FaissIndex. To do so you can run `pip install faiss-cpu` or `pip install faiss-gpu`\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 236\u001b[0m )\n\u001b[1;32m 237\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mImportError\u001b[0m: You must install Faiss to use FaissIndex. To do so you can run `pip install faiss-cpu` or `pip install faiss-gpu`", "", "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n" ], "errorDetails": { "actions": [ { "action": "open_url", "actionText": "Open Examples", "url": "/notebooks/snippets/importing_libraries.ipynb" } ] } } ] }, { "cell_type": "code", "source": [ "question = \"how can i load a dataset offline\"\n", "question_embedding = get_embeddings([question]).cpu().detach().numpy()\n", "question_embedding.shape" ], "metadata": { "id": "Y1ydH_fxx7C_", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "991a19b8-1425-4165-ae38-62da8d18c315" }, "execution_count": 74, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(1, 768)" ] }, "metadata": {}, "execution_count": 74 } ] }, { "cell_type": "code", "source": [ "scores, samples = embeddings_dataset.get_nearest_examples(\"embeddings\", question_embedding, k=5)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 380 }, "id": "28FQY6Wu5NsM", "outputId": "21874add-4625-4691-8849-ace99dad3dc9" }, "execution_count": 75, "outputs": [ { "output_type": "error", "ename": "MissingIndex", "evalue": "ignored", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mMissingIndex\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msamples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0membeddings_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_nearest_examples\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"embeddings\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquestion_embedding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\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/datasets/search.py\u001b[0m in \u001b[0;36mget_nearest_examples\u001b[0;34m(self, index_name, query, k)\u001b[0m\n\u001b[1;32m 654\u001b[0m \u001b[0mexamples\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mThe\u001b[0m \u001b[0mretrieved\u001b[0m \u001b[0mexamples\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 655\u001b[0m \"\"\"\n\u001b[0;32m--> 656\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_index_is_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex_name\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 657\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquery\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 658\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mNearestExamplesResults\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindices\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/datasets/search.py\u001b[0m in \u001b[0;36m_check_index_is_initialized\u001b[0;34m(self, index_name)\u001b[0m\n\u001b[1;32m 380\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_index_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 381\u001b[0m raise MissingIndex(\n\u001b[0;32m--> 382\u001b[0;31m \u001b[0;34mf\"Index with index_name '{index_name}' not initialized yet. Please make sure that you call `add_faiss_index` or `add_elasticsearch_index` first.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 383\u001b[0m )\n\u001b[1;32m 384\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mMissingIndex\u001b[0m: Index with index_name 'embeddings' not initialized yet. Please make sure that you call `add_faiss_index` or `add_elasticsearch_index` first." ] } ] }, { "cell_type": "code", "source": [ "import pandas as pd\n", "samples_df = pd.DataFrame.from_dict(samples)\n", "samples_df[\"scores\"] = scores\n", "samples_df.sort_values(\"scores\", ascending=False, inplace=True)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 217 }, "id": "b3dGeZZu4jhq", "outputId": "1e45a149-84f0-49a5-ac27-8aec7321c7b9" }, "execution_count": 76, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "ignored", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msamples_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msamples\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 3\u001b[0m \u001b[0msamples_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"scores\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0msamples_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"scores\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mNameError\u001b[0m: name 'samples' is not defined" ] } ] }, { "cell_type": "code", "source": [ "for _, row in samples_df.iterrows():\n", " print(row.comments)\n", " print(row.scores)\n", " " ], "metadata": { "id": "7cQ98faN5glo" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "as-FLDKJ7TnJ" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "!curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash\n", "!sudo apt-get install git-lfs" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Q31jTmba7b2J", "outputId": "317df20f-115d-42a4-b59e-84941dce23d9" }, "execution_count": 78, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Detected operating system as Ubuntu/bionic.\n", "Checking for curl...\n", "Detected curl...\n", "Checking for gpg...\n", "Detected gpg...\n", "Running apt-get update... done.\n", "Installing apt-transport-https... done.\n", "Installing /etc/apt/sources.list.d/github_git-lfs.list...done.\n", "Importing packagecloud gpg key... done.\n", "Running apt-get update... done.\n", "\n", "The repository is setup! You can now install packages.\n", "Reading package lists... Done\n", "Building dependency tree \n", "Reading state information... Done\n", "The following NEW packages will be installed:\n", " git-lfs\n", "0 upgraded, 1 newly installed, 0 to remove and 85 not upgraded.\n", "Need to get 6,526 kB of archives.\n", "After this operation, 14.7 MB of additional disk space will be used.\n", "Get:1 https://packagecloud.io/github/git-lfs/ubuntu bionic/main amd64 git-lfs amd64 3.0.2 [6,526 kB]\n", "Fetched 6,526 kB in 1s (5,889 kB/s)\n", "debconf: unable to initialize frontend: Dialog\n", "debconf: (No usable dialog-like program is installed, so the dialog based frontend cannot be used. at /usr/share/perl5/Debconf/FrontEnd/Dialog.pm line 76, <> line 1.)\n", "debconf: falling back to frontend: Readline\n", "debconf: unable to initialize frontend: Readline\n", "debconf: (This frontend requires a controlling tty.)\n", "debconf: falling back to frontend: Teletype\n", "dpkg-preconfigure: unable to re-open stdin: \n", "Selecting previously unselected package git-lfs.\n", "(Reading database ... 155238 files and directories currently installed.)\n", "Preparing to unpack .../git-lfs_3.0.2_amd64.deb ...\n", "Unpacking git-lfs (3.0.2) ...\n", "Setting up git-lfs (3.0.2) ...\n", "Git LFS initialized.\n", "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n" ] } ] }, { "cell_type": "code", "source": [ "from huggingface_hub import Repository\n", "repo_url = \"https://huggingface.co/datasets/SebastianS/github-issues\"\n", "repo = Repository(local_dir=\"github-issues\", clone_from=repo_url)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "DipwsIQj7CmB", "outputId": "92357098-6ece-4df9-d691-43ebf50d79cc" }, "execution_count": 86, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/content/github-issues is already a clone of https://huggingface.co/datasets/SebastianS/github-issues. Make sure you pull the latest changes with `repo.git_pull()`.\n" ] } ] }, { "cell_type": "code", "source": [ "!cp part5.ipynb ./github-issues" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "VVMTI9Td505h", "outputId": "668d99f1-6058-485e-aa86-bf86b13f0cf4" }, "execution_count": 89, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "cp: cannot stat 'part5.ipynb': No such file or directory\n" ] } ] }, { "cell_type": "code", "source": [ "repo.push_to_hub()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 137, "referenced_widgets": [ "942e6ad22f044d44a0fe23ce819929ec", "b4dcfc1489c149c3b65278935daabfab", "fe6cb47fda0c4cbe81f7a1155b62fc49", "6edd4d9e1fe64e21b6184ad19b0df245", "ed04f4f6194642209823867da637ea61", "c99de0e9bf274f798632aa01086fb54d", "6755bb29e4d149dbbac4cc86cadece2a", "a1447add35f44261beb0db15ad0b60a6", "48d19771cb3d49df8e787dd22a83858e", "a407c45fbc254ed78dec7fa950afc716", "1baee4e920624de1973950a673643baf" ] }, "id": "eCXoZ2UK7pdD", "outputId": "b399cfc5-4ec4-42cb-ade4-09928681c0d1" }, "execution_count": 88, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "942e6ad22f044d44a0fe23ce819929ec", "version_minor": 0, "version_major": 2 }, "text/plain": [ "Upload file embedded_dataset.jsonl: 0%| | 32.0k/45.6M [00:00 main\n", "\n" ] }, { "output_type": "execute_result", "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'https://huggingface.co/datasets/SebastianS/github-issues/commit/2ff0a712964f9e687a205768fa847585dfa38c28'" ] }, "metadata": {}, "execution_count": 88 } ] }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "Q-OHUM948Alm" }, "execution_count": null, "outputs": [] } ] }