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"grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "a407c45fbc254ed78dec7fa950afc716": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "StyleView", + "_model_name": "DescriptionStyleModel", + "description_width": "", + "_view_module": "@jupyter-widgets/base", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.2.0", + "_model_module": "@jupyter-widgets/controls" + } + }, + "1baee4e920624de1973950a673643baf": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + } + } + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JPdOQa337CFz", + "outputId": "7218cee2-fca5-4bf5-a7da-77d15168dbd8" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting datasets\n", + " Downloading datasets-1.17.0-py3-none-any.whl (306 kB)\n", + "\u001b[?25l\r\u001b[K |█ | 10 kB 24.4 MB/s eta 0:00:01\r\u001b[K |██▏ | 20 kB 27.5 MB/s eta 0:00:01\r\u001b[K |███▏ | 30 kB 31.8 MB/s eta 0:00:01\r\u001b[K |████▎ | 40 kB 23.0 MB/s eta 0:00:01\r\u001b[K |█████▍ | 51 kB 9.2 MB/s eta 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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 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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 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}\n\u001b[1;32m 484\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--> 485\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 486\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 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tokenize_and_split, batched=True, remove_columns=drug_dataset[\"train\"].column_names\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81, + "referenced_widgets": [ + "832da29a59414c4e96a6a84209b63b02", + "b4fae8961b334965b4c498ebac76b45e", + "3a07c7f3b06f42028bb9537332b03260", + "16630bd3a3b0407e8745e8bbfcf674d1", + "e847d3429fa44a81b15ad04c357f84a4", + "946270bdde754cbd93bbe0c69356b22e", + "c697a08fd94e44628f2d953e15464949", + "cb73171a78514f029bb81f1bc62f9e3b", + "ff8de08b534d4eb5ae50b82a698ebb77", + "ed2b2a27fff7411b8df4b88a78ed224b", + "b9cdbbf2a24341bcb8f83b1ced79aad3", + "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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" inflating: drugsComTrain_raw.tsv \n" + "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": [ - "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\")" + "drug_dataset_clean.save_to_disk(\"drug_reviews\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 185, + "height": 81, "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" + "b5a34cbe2cd942c3867f70b880eaec52", + "fdcd0dd845244debb0421558c01143c9", + "26eb07fddf604af2bf4b5e0f3b833d77", + "fe9b6afb8bcc4cf5bdf4ca9108516bb2", + "07978b9d0b344bebb081a684956e6c2e", + "afbd8f7e8bde4425b08646d36942595e", + "2b6348b223c04f97b36e9faa4561ad2b", + "3bf9dc19420441f195b7be335214c0e4", + "8a80497864db4d2fbebcdb6ef1495ad2", + "d485d46b139f47d3af6573ba69c5d6c3", + "f41cc85ff60f494da53efdcb6153be61", + "a8b786a21777429284684413989c84a7", + "8197d978edc44c2389a0dedac054f02e", + "abff636ad0c74ccfb5e8b626bb9e9822", + "10fe3c970f6a4e41a13126be433d019c", + "c26e75aaf473465fbb37238d52f6ec26", + "8ec16acfc7ad42be85907a2619aa335e", + "0191473df6664fe3bd25b5bcd9d1e2fa", + "27aa40c90b314b76ac60f9caaef3dc1d", + "cf85e34f2625470c902f5e8516d40fec", + "5af4486076e247eba2443641b19061f5", + "b87fcc903ced4949bfbac77b826bf47b" ] }, - "id": "MIhgoeS47yGQ", - "outputId": "1da4bd07-9f7d-4762-c471-bf21070d71db" + "id": "3Aer00sgSm5V", + "outputId": "de60d7c2-7da5-479b-f833-0d9d5dba6da9" }, - "execution_count": 5, + "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)" + "from datasets import load_dataset, load_from_disk, DownloadConfig\n", + "download_config = DownloadConfig(delete_extracted=True)\n", + "data_files = \"drug_reviews\"\n", + "pubmed_dataset = load_dataset(\"csv\", data_files=\"drugsComTrain_raw.tsv\", delimiter=\"\\t\", download_config=download_config)\n", + "# pubmed_dataset = load_dataset(\"dataset\", data_files=data_files, split=\"train\")\n", + "pubmed_dataset = pubmed_dataset[\"train\"]\n", + "pubmed_dataset" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 98, + "height": 173, "referenced_widgets": [ - "19972ac7a9524fae88bec023d5624134", - "bcb77810547a49d784d8c8987055857f", - "47ea578ad8e6466b9ed354e382d8c060", - "1a16cbf23aea441ba8afa1e1b40562bb", - "67b0d0d7407a450ea786fc685d3d20c9", - "b9afc23a3cc7467abb9863b0dde54f68", - "77e0cf1cc69843c98529fb2e90ac34d1", - "c38efe098a7346aa93ffa8e629f4de23", - "681b924c19944c3a948de88f05cb028b", - "860495d9fa444c7a8c4d411aad233865", - "5a15918a9e544ac586f1798602cfff8d", - "b2e8cd19f16843a6a85c23cc607bccf3", - "ec7b9dd6548e41b39a0cec8bee978ad6", - "45155064990a4a3ba6a357be809427d6", - "4aa1c46e33fd46dba8af7cf0bb5d3994", - "ff8a6ab06c2c4669ade31e65adf6a6f2", - "4ebacf83a7094147b16b8b4120e56e9d", - "5a2667b5f4424819b1e23889697d3299", - "cc022b7d144749de93fbfed810aa6c97", - "8c9ed4f2370d4deaaf5f54ede278f2f6", - "2587b7d3a95a49eb8ac86a2abaf38f7b", - "8ecd319a00bf472eb67b4f2065876f66" + "1b69b00ce99b4937af32d682c1ffa701", + "a24ca26315d848af8b66c16fc591e7da", + "51059fda818749ff853c38fb2c021e28", + "e308a8ea2801457c92d21c2b63b49bf7", + "7df1ae7227434c00ae86e5a1241e4cbf", + "dba4013c75f74b1a9b3e2358c11b6786", + "84f33c0796cd4c30a3cf2a74967569ef", + "0a369e5e7d714be4bb4eafa504abb1e4", + "5fd7c011012244f698d4c81a39c1869f", + "e4cf3f6f0a92448b887ca1105a993a92", + "3c2fe96a92ca4058903e1c4c94f2f262" ] }, - "id": "GS2CTTiZFyMh", - "outputId": "a3f6e7f1-c758-4553-bbf4-06db0fadf09d" + "id": "8v7AJ0rBk1nT", + "outputId": "3027b461-5990-4839-f4f7-852b3a9b7311" }, - "execution_count": 14, + "execution_count": null, "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Using custom data configuration default-fd5f2eb4422fa8dd\n", + "Reusing dataset csv (/root/.cache/huggingface/datasets/csv/default-fd5f2eb4422fa8dd/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e)\n" + ] + }, { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "19972ac7a9524fae88bec023d5624134", + "model_id": "1b69b00ce99b4937af32d682c1ffa701", "version_minor": 0, "version_major": 2 }, "text/plain": [ - " 0%| | 0/161 [00:00" ] }, "metadata": {}, - "execution_count": 22 + "execution_count": 117 } ] }, { "cell_type": "code", "source": [ - "tokenized_dataset = drug_dataset.map(tokenize_and_split, batched=True)" + "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": 273, - "referenced_widgets": [ - "7a1af583d5744f94ba60a47865ad6c6a", - "30fb898b345d4b54a34df046b2485fed", - "a19dbd71fc444f3482f4f30fa70bb0b9", - "a29ff675c747452a988d6f2b7f395ab6", - "be34e5ec5ad5434383b2fe4934622433", - "e506a754207b44d0ab6dacba91eeb516", - "3093806fd7b8499383b2e3be3ab50df5", - "7c2b0441add44cc0aebb96ecbdfe15d1", - "c1e0c4f49fb846b7a5559516f0c7f4c6", - "168a6ecadd52454f837c6651b8854fef", - "32cd166f43da4b93a4f79a1b8c29952b" - ] + "height": 200 }, - "id": "kDkqT-jwVBHK", - "outputId": "60ed0e19-32ca-461b-ce58-84031233844e" + "id": "AxS8tNj_wnUV", + "outputId": "3c5bcd24-b262-4248-a239-67fe381dc431" }, - "execution_count": 23, + "execution_count": null, "outputs": [ - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "7a1af583d5744f94ba60a47865ad6c6a", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - " 0%| | 0/139 [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", - 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"\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 \u001b[0;32mfor\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.Table.from_pydict\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.Table.from_arrays\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pyarrow/table.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.Table.validate\u001b[0;34m()\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pyarrow/error.pxi\u001b[0m in \u001b[0;36mpyarrow.lib.check_status\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mArrowInvalid\u001b[0m: Column 1 named condition expected length 1463 but got length 1000" + "\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" ] } ] @@ -18211,1580 +24964,1235 @@ { "cell_type": "code", "source": [ - "tokenized_dataset = drug_dataset.map(\n", - " tokenize_and_split, batched=True, remove_columns=drug_dataset[\"train\"].column_names\n", - ")" + "shuffled_dataset = pubmed_dataset_streamed.shuffle(buffer_size=1000,seed=42)" ], "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81, - "referenced_widgets": [ - "832da29a59414c4e96a6a84209b63b02", - "b4fae8961b334965b4c498ebac76b45e", - "3a07c7f3b06f42028bb9537332b03260", - "16630bd3a3b0407e8745e8bbfcf674d1", - "e847d3429fa44a81b15ad04c357f84a4", - "946270bdde754cbd93bbe0c69356b22e", - "c697a08fd94e44628f2d953e15464949", - "cb73171a78514f029bb81f1bc62f9e3b", - "ff8de08b534d4eb5ae50b82a698ebb77", - "ed2b2a27fff7411b8df4b88a78ed224b", - "b9cdbbf2a24341bcb8f83b1ced79aad3", - "55765c91f69a434bbeee0ec4c958ec4a", - "124c93b98e4849d5a6ffae08778c30af", - "90396fc7a5304bf5b9e40079bf01fcca", - "a23c1206f8bb4e8d94e7124c8876aabb", - "e421f3c846f84af59fec6d0df18165ca", - "82752376f32b43068618d4c6e171b4b6", - "d15b58a1ebdc4a99ae5f17dadd2b049b", - "48409e890cc043989cba4e0cde911244", - "b8c4dee5841246deb177038bd2a6b164", - "ed88ac6ac20f4abea6b1a6fafe431278", - "782b9b92a8634fe3bb1a6b5275f19cff" - ] - }, - "id": "QNmSMvN0VYx9", - "outputId": "53ec8ab0-0b54-4370-aeaa-d37f28d33240" + "id": "Zqe95BHwxXvy" }, - "execution_count": 24, - "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
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1depression8023
2acne5209
3anxiety4991
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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": 37 + "execution_count": 6 } ] }, { "cell_type": "code", "source": [ - "from datasets import Dataset\n", + "from transformers import AutoTokenizer\n", "\n", - "freq_dataset = Dataset.from_pandas(frequencies)\n", - "freq_dataset" + "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")" ], "metadata": { "colab": { - "base_uri": "https://localhost:8080/" + "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": "Iu8Mq_3DHiFF", - "outputId": "4c07ad7d-66f0-4fc3-818b-724ec69eeee8" + "id": "XT5LAOV1MLwv", + "outputId": "c095e654-38c3-47f9-838f-0fe4f6deaa54" }, - "execution_count": 38, + "execution_count": null, "outputs": [ { - "output_type": "execute_result", + "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\n", - "
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drugNamerating
2060OxyContin8.586667
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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": "oDnY6TawT6W-", - "outputId": "5131b939-5c7e-4b0e-b705-f2cdbdb89e21" + "id": "eE9KLjNAkYn3", + "outputId": "472503dc-6cd1-4eac-b05f-cec245c1648d" }, - "execution_count": 86, + "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a9298b0d5f8f468492885c1bf0ed223d", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "Creating json from Arrow format: 0%| | 0/12 [00:00\n", + "
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\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + " \n", + " " + ], "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", - "})" + " 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": 89 + "execution_count": 35 } ] }, { - "cell_type": "markdown", + "cell_type": "code", "source": [ - "# Big data" + "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": "4ZXHOqwlk2w5" - } + "id": "V8_H9966mkTK" + }, + "execution_count": null, + "outputs": [] }, { "cell_type": "code", "source": [ - "!pip install zstandard" + "issues_df[\"duration\"] = issues_df.apply(lambda row: duration_hrs(row), axis=1)" ], "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "u7O68gmyVX37", - "outputId": "8428f6f1-3f45-4e35-90a4-9426501aec43" + "id": "PKU947xaqlki" }, - "execution_count": 90, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Collecting zstandard\n", - " Downloading zstandard-0.16.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.9 MB)\n", - "\u001b[K |████████████████████████████████| 2.9 MB 7.7 MB/s \n", - "\u001b[?25hInstalling collected packages: zstandard\n", - "Successfully installed zstandard-0.16.0\n" - ] - } - ] + "execution_count": null, + "outputs": [] }, { "cell_type": "code", "source": [ - "from datasets import load_dataset, load_from_disk, DownloadConfig\n", - "download_config = DownloadConfig(delete_extracted=True)\n", - "data_files = \"drug_reviews\"\n", - "pubmed_dataset = load_dataset(\"csv\", data_files=\"drugsComTrain_raw.tsv\", delimiter=\"\\t\", download_config=download_config)\n", - "# pubmed_dataset = load_dataset(\"dataset\", data_files=data_files, split=\"train\")\n", - "pubmed_dataset = pubmed_dataset[\"train\"]\n", - "pubmed_dataset" + "issues_df[issues_df[\"duration\"].notna()][\"duration\"].mean()" ], "metadata": { "colab": { - "base_uri": "https://localhost:8080/", - "height": 173, - "referenced_widgets": [ - "1b69b00ce99b4937af32d682c1ffa701", - "a24ca26315d848af8b66c16fc591e7da", - "51059fda818749ff853c38fb2c021e28", - "e308a8ea2801457c92d21c2b63b49bf7", - "7df1ae7227434c00ae86e5a1241e4cbf", - "dba4013c75f74b1a9b3e2358c11b6786", - "84f33c0796cd4c30a3cf2a74967569ef", - "0a369e5e7d714be4bb4eafa504abb1e4", - "5fd7c011012244f698d4c81a39c1869f", - "e4cf3f6f0a92448b887ca1105a993a92", - "3c2fe96a92ca4058903e1c4c94f2f262" - ] + "base_uri": "https://localhost:8080/" }, - "id": "8v7AJ0rBk1nT", - "outputId": "3027b461-5990-4839-f4f7-852b3a9b7311" + "id": "EK8exlket49h", + "outputId": "fe9187fd-345d-461d-bc67-d40a64b8eecf" }, - "execution_count": 108, + "execution_count": null, "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "Using custom data configuration default-fd5f2eb4422fa8dd\n", - "Reusing dataset csv (/root/.cache/huggingface/datasets/csv/default-fd5f2eb4422fa8dd/0.0.0/6b9057d9e23d9d8a2f05b985917a0da84d70c5dae3d22ddd8a3f22fb01c69d9e)\n" - ] - }, - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1b69b00ce99b4937af32d682c1ffa701", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - " 0%| | 0/1 [00:00 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": 116 + "execution_count": 72 } ] }, { "cell_type": "code", "source": [ - "from transformers import AutoTokenizer\n", - "\n", - "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")" + "issues_with_comments_dataset = issues_dataset.map(\n", + " lambda x: {\"comments\": get_comments(x[\"number\"])}\n", + ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 162, + "height": 49, "referenced_widgets": [ - "a48ce4f2e90447a8bb4baadbd4bea86a", - "a206cae3de0949fa9bee7f134230bdf6", - "9cf97e9dc35d493eb155c4589d426a38", - "50808a803093414786e5f4d2fc3aa53d", - "b6a4bf539353489cae6d4f3680b0bd61", - "f3a7b3019c174ecc9f3659d63d02a2ad", - "e37779a755b54e21a8dbd9e2a60a6951", - "69fa53e6221b43998f587586dee215c0", - "badb47900842420e8c475dd6e044e7cf", - "3c6aeb4bb17142a68c7235e96aa1abd9", - "da36a09037e9428a9a6670adde94b140", - "bc7bda046b5f4b1a8428a50805deed01", - "9ed51d276c4a43da9621af508a7c4c7e", - "118b0f07a1b34b6b98dcb4277a9a1264", - "5bc46272d4494004a64cc56824a2bf9b", - "93c712857ba74ad6b8e2e955faaa77e7", - "1d9f3099130c463abd93f73782b706fe", - "8db07a9a4b0a42008fcb146e1c8761e6", - "d7d9b1eba35e4b679e4dc0b9acc16b39", - "0964c3577a754b358d7b40e65b10124c", - "6f53d65eb9d943a5915bf011c0f6c890", - "08fb721cf4bf42f2b86a92f0dd643414", - "b525c936086c4e28aeaac97e74de39a5", - "7abf4668e2ba48bba925ffeedfec9d17", - "15097452273148f9ba21edff15cdc2de", - "db141d80b5c947cbbb5718929e60fa43", - "0c9885df82f34d8d9a5f1271c8636822", - "0b1b4e0890714923be47df7f4ee3fec1", - "5e0d70c0fa7848459838dd253a626b86", - "0b4ea9898ff244e7bc51417733722a2b", - "e5ee580047ca4feb9357c51552f89d7a", - "a602cbb82b134f81979e0d4a39882ad1", - "df974b4cbcdc4962ac68883db9ac7044", - "131fbe115fcb4be5b237db3e2a9aac55", - "05e4c6cb66744fce8d5f9b751e0fb9c2", - "ecbdfd08a5cc4b499ebf7be0d578415c", - "174039ae3f1845068a6ee7cec31bea80", - "4c26ac871f3047e0bc8dc8679de5601c", - "7e89b799a7d8426ebb3308792f8f9583", - "5facfd55034a4db9beb4dd44534cb4f3", - "957b851d77314c5d84c3f6a71192a3fc", - "75fc83fe2b28412da944756b5457c828", - "b5989f99628d4cf48340f6c7576d40be", - "02cf3131555e4c87a95e41eb54d36baf" + "a4d3d0fa7f714e2d87a9f760ff3c5d9b", + "a129ce2bfa004224b58bb65349c4d437", + "0941d09fbad14fcca7e68a3f79411fbb", + "0c1735142887463b825f5a7f893b3ad3", + "54de99c728f244c6b3ab425a478a9007", + "bdfa6f473d1948e7aa2f234c4199e2d5", + "8b40c8cbf9704904abab534e46541402", + "be5e8a2338694af1b0032d9b03239441", + "0f9ac20005b743cea31c8a3f8dbf8a35", + "5aec9265276e47088fe5dd7afb362c91", + "21a5001315e8473e86b9edd2f6925b1e" ] }, - "id": "oLefyDxpv4lO", - "outputId": "809071bf-cb4d-4472-c9c0-0f1a7f52911f" + "id": "zIOx9i9A3uRy", + "outputId": "d7ffa78a-5d79-466e-9eec-040d34a08544" }, - "execution_count": 117, + "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a48ce4f2e90447a8bb4baadbd4bea86a", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "Downloading: 0%| | 0.00/28.0 [00:00" + "15520904" ] }, "metadata": {}, - "execution_count": 117 + "execution_count": 74 } ] }, { "cell_type": "code", "source": [ - "tokenized_dataset = pubmed_dataset_streamed.map(lambda x: tokenizer(x[\"review\"]), batched=True)\n", - "next(iter(tokenized_dataset))" + "from huggingface_hub import list_datasets\n", + "all_datasets = list_datasets()\n", + "print(len(all_datasets))\n", + "print(all_datasets[0])" ], "metadata": { "colab": { - "base_uri": "https://localhost:8080/", - "height": 200 + "base_uri": "https://localhost:8080/" }, - "id": "AxS8tNj_wnUV", - "outputId": "3c5bcd24-b262-4248-a239-67fe381dc431" + "id": "z6yvIG6o7TCj", + "outputId": "bed38f65-9d98-41bb-8345-25f1980d0226" }, - "execution_count": 134, + "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" + "output_type": "stream", + "name": "stdout", + "text": [ + "2288\n", + "Dataset Name: 0n1xus/codexglue, Tags: []\n" ] } ] @@ -19833,105 +26239,150 @@ { "cell_type": "code", "source": [ - "shuffled_dataset = pubmed_dataset_streamed.shuffle(buffer_size=1000,seed=42)" + "from huggingface_hub import notebook_login\n", + "notebook_login()" ], "metadata": { - "id": "Zqe95BHwxXvy" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 499, + "referenced_widgets": [ + "e253562c40e84cd48a591ae8fbdd8882", + "359fee347b6b474c9c25bbfeed583e7e", + "90bd80166ee046a28408dcd6a180169e", + "966207503baa469ca8542121784d4c7b", + "5489866204e3458ba355fc92623b39f5", + "a8cceca9cd89483aa672c2e9f6f762ce", + "5c534ca7a343437393f5ec002e1440dd", + "cd731edd2721492287102934c07eaac7", + "d1848e6bd0c745e9bca74ed3e018d4fe", + "48f5dec69f044020ad043b38b9adea9c", + "546457186e5d4e3b99309970e3039d6d", + "cf693a04421c45ac98aea78755933005", + "f37a9d01303b4e028220184936610ddc", + "c41e4dddbc3d424ca37b33da33cf94a2", + "26c61dae39f44d459e650b591070071c", + "cf5a1cf028de4526b460b836fcbae866", + "ed2e5c8596f1494a843037a77422f296" + ] + }, + "id": "QKZqAOnj7or1", + "outputId": "679f9c6c-ac42-4bf9-9267-4b2ed7790c70" }, - "execution_count": 142, - "outputs": [] + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Login successful\n", + "Your token has been saved to /root/.huggingface/token\n", + "\u001b[1m\u001b[31mAuthenticated through git-credential store but this isn't the helper defined on your machine.\n", + "You might have to re-authenticate when pushing to the Hugging Face Hub. Run the following command in your terminal in case you want to set this credential helper as the default\n", + "\n", + "git config --global credential.helper store\u001b[0m\n" + ] + } + ] }, { "cell_type": "code", "source": [ - "train_dataset = shuffled_dataset.skip(500)\n", - "validation_dataset = shuffled_dataset.take(500)" + "from huggingface_hub import create_repo\n", + "repo_url = create_repo(name=\"github-issues\", repo_type=\"dataset\")\n", + "repo_url" ], "metadata": { - "id": "zsGnBEWC1ZyZ" + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "id": "7jx2VHUH81S7", + "outputId": "59f92a76-dd7a-4ef3-a9a1-2d76ffda0cc9" }, - "execution_count": 143, - "outputs": [] + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "'https://huggingface.co/datasets/SebastianS/github-issues'" + ] + }, + "metadata": {}, + "execution_count": 78 + } + ] }, { "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))" + "!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": "_LZd827S2bA5", - "outputId": "f7333dfa-f5a3-46d6-961f-01046521af55" + "id": "4ul_ECsp-wkB", + "outputId": "f0b9d333-1fec-400e-998c-97ee18c6d3e3" }, - "execution_count": 149, + "execution_count": null, "outputs": [ { "output_type": "stream", - "name": "stderr", + "name": "stdout", "text": [ - "Using custom data configuration default-f7dc8c58638ca9ea\n" + "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" ] - }, - { - "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))" + "from huggingface_hub import Repository\n", + "repo = Repository(local_dir=\"github-issues\", clone_from=repo_url)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "mfBA20l52e4G", - "outputId": "460b0692-1377-4cb4-fb1d-df1ae577dd08" + "id": "qiAZsD4D_jLy", + "outputId": "a2df6c1a-4363-45f9-80c2-393cd83f435e" }, - "execution_count": 151, + "execution_count": null, "outputs": [ { - "output_type": "execute_result", - "data": { - "text/plain": [ - "[]" - ] - }, - "metadata": {}, - "execution_count": 151 + "output_type": "stream", + "name": "stderr", + "text": [ + "Cloning https://huggingface.co/datasets/SebastianS/github-issues into local empty directory.\n" + ] } ] }, { "cell_type": "code", "source": [ - "" + "!cp issues-datasets-with-comments.jsonl github-issues/" ], "metadata": { - "id": "yxYkiOGVNzAj" + "id": "RyCYaKiNACfJ" }, "execution_count": null, "outputs": [] @@ -19939,120 +26390,155 @@ { "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))" + "repo.lfs_track(\"*.jsonl\")" + ], + "metadata": { + "id": "zvelplR0AZol" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "repo.push_to_hub()" ], "metadata": { "colab": { - "base_uri": "https://localhost:8080/" + "base_uri": "https://localhost:8080/", + "height": 165, + "referenced_widgets": [ + "8eaacdf4a63a455998f33052aaff3494", + "17b0494b35d943439e6cb0d9312ec97b", + "9ef4b8ca89d541c39befbd2ecd567cef", + "13704a202e774643beee0d7d5fbd9e5e", + "60f214d35a864ddf987527a9dbe18f0f", + "e47ced2ed6e54e48825394b93f4ef68d", + "a05e829e36d5429a8abac3c7a7f16e9a", + "9364020b88ac4629bba621cb423338bc", + "98bde718736f4f19a82042d5a9334f39", + "f2695d9b69054c7583e95dc532eea4da", + "cfe974f2625d4fe7b0d06b7f1065555c" + ] }, - "id": "RA3SWb0Y3V0S", - "outputId": "823dd2c2-1025-4bf8-ee09-40681e3d73b7" + "id": "EM5B6PG5BFEy", + "outputId": "31a44344-8424-416f-e231-3881b4680bfb" }, - "execution_count": 6, + "execution_count": null, "outputs": [ + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8eaacdf4a63a455998f33052aaff3494", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "Upload file issues-datasets-with-comments.jsonl: 0%| | 32.0k/14.8M [00:00 main\n", + "\n" + ] + }, { "output_type": "execute_result", "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, "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.)'}" + "'https://huggingface.co/datasets/SebastianS/github-issues/commit/44b97cc6a8ee299421dc01cd60ef25fc7681a0a5'" ] }, "metadata": {}, - "execution_count": 6 + "execution_count": 86 } ] }, { "cell_type": "code", "source": [ - "from transformers import AutoTokenizer\n", - "\n", - "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")" + "remote_dataset = load_dataset(\"SebastianS/github-issues\", split=\"train\")\n", + "remote_dataset" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", - "height": 145, + "height": 255, "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" + "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": "XT5LAOV1MLwv", - "outputId": "c095e654-38c3-47f9-838f-0fe4f6deaa54" + "id": "TH1RrW6TBJlF", + "outputId": "dc729dc2-a807-4daf-cacd-e9762bb4792d" }, - "execution_count": 8, + "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 main\n", + "\n" + ] + }, { "output_type": "execute_result", "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, "text/plain": [ - "[{'id': 0,\n", - " 'text': '时间可以被缩短，但过程不可以被省略，只有真正为社会创造价值的企业才能基业长青。大巧不工，重剑无锋，企业最终还是要用业绩和结果说话。一级a卡片在线观看通过产品打磨、团队搭建、市场营销、经营管理等过程，秉承着品质永恒，承诺不变的经营理念，鲁诺为全国众多行业用户及客户提供车辆财产及人身安全保障。业务涉及汽车信贷产品风控服务体系搭建与整合；汽车安全管理服务整体解决方案；安全管理设备研发及生产；信息平台研发及运维。\\n大道鲁商、千金一诺。鲁诺集��秉承着钻研、团结、求实、奉献的企业精神，践行着对社会负责、对客户负责、对合作伙伴负责的使命，创新发展，打造贴合用户需求的系列汽车风控产品，在汽车安全管理风控市场独领风骚。回顾一年，我们收获了更多真诚的合作伙伴、乐于奉献的企业员工、还有业界内专业用户广泛的赞誉。'},\n", - " {'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.)'},\n", - " {'id': 0,\n", - " 'text': 'Dosierförderbänder Getriebe Entwässerungssiebmaschine USE 1400 x 3500 mm Eimerkettenbagger Entstaubungsanlage'}]" + "'https://huggingface.co/datasets/SebastianS/github-issues/commit/bc18afe169e74ddd9c0ca76227d15bd61495b46b'" ] }, "metadata": {}, - "execution_count": 18 + "execution_count": 90 } ] }, { "cell_type": "markdown", "source": [ - "# creating datasets" + "# Semantic search" ], "metadata": { - "id": "KBqRM9_qdclU" + "id": "_uWS4VQzeCUv" } }, { "cell_type": "code", "source": [ - "GH_ACCESS_TOKEN = \"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\"\n", - "headers = {\"Authorization\": f\"token {GH_ACCESS_TOKEN}\"}" - ], - "metadata": { - "id": "E6lAOiWlQeHu" - }, - "execution_count": 3, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "!pip install requests transformers datasets" - ], - "metadata": { - "id": "jzjoqpsUgcbp" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "import requests\n", - "\n", - "url = \"https://api.github.com/repos/huggingface/datasets/issues?page=1&per_page=1\"\n", - "response = requests.get(url)" - ], - "metadata": { - "id": "zSTALglugeKv" - }, - "execution_count": 5, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "response.json()" + "!pip install transformers datasets" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, - "id": "8OdQMsFGg9JZ", - "outputId": "247d0e4c-a030-4c29-c706-533efe474a2a" + "id": "T-dJ9wDFeJIl", + "outputId": "d4bc7253-3a98-4cb4-f644-1cb66dee0057" }, - "execution_count": 6, + "execution_count": null, "outputs": [ { - "output_type": "execute_result", - "data": { - "text/plain": [ - "[{'active_lock_reason': None,\n", - " 'assignee': None,\n", - " 'assignees': [],\n", - " 'author_association': 'NONE',\n", - " 'body': \"`textwrap.dedent` works based on the spaces at the beginning. Before this change, there wasn't any space.\",\n", - " 'closed_at': None,\n", - " 'comments': 0,\n", - " 'comments_url': 'https://api.github.com/repos/huggingface/datasets/issues/3486/comments',\n", - " 'created_at': '2021-12-27T11:20:36Z',\n", - " 'draft': False,\n", - " 'events_url': 'https://api.github.com/repos/huggingface/datasets/issues/3486/events',\n", - " 'html_url': 'https://github.com/huggingface/datasets/pull/3486',\n", - " 'id': 1089171551,\n", - " 'labels': [],\n", - " 'labels_url': 'https://api.github.com/repos/huggingface/datasets/issues/3486/labels{/name}',\n", - " 'locked': False,\n", - " 'milestone': None,\n", - " 'node_id': 'PR_kwDODunzps4wTNd1',\n", - " 'number': 3486,\n", - " 'performed_via_github_app': None,\n", - " 'pull_request': {'diff_url': 'https://github.com/huggingface/datasets/pull/3486.diff',\n", - " 'html_url': 'https://github.com/huggingface/datasets/pull/3486',\n", - " 'merged_at': None,\n", - " 'patch_url': 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"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", + "\u001b[?25hRequirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.8.2)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from 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transformers-4.15.0 xxhash-2.0.2 yarl-1.7.2\n" + ] } ] }, { "cell_type": "code", "source": [ - "import time\n", - "import math\n", - "from pathlib import Path\n", - "import pandas as pd\n", - "from tqdm.notebook import tqdm\n", - "\n", - "def fetch_issues(\n", - " owner=\"huggingface\",\n", - " repo=\"datasets\",\n", - " num_issues=10000,\n", - " rate_limit=5000,\n", - " issues_path=Path(\".\")\n", - "):\n", - " issues_path.mkdir(exist_ok=True)\n", - " batch = []\n", - " all_issues = []\n", - " per_page = 100 # Number of issues to return per page\n", - " num_pages = math.ceil(num_issues / per_page)\n", - " base_url = \"https://api.github.com/repos\"\n", - "\n", - " for page in tqdm(range(num_pages)):\n", - " # Query with state=all to get both open and closed issues\n", - " query = f\"issues?page={page}&per_page={per_page}&state=all\"\n", - " issues = requests.get(f\"{base_url}/{owner}/{repo}/{query}\", headers=headers)\n", - " batch.extend(issues.json())\n", - "\n", - " if len(batch) > 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", + "from huggingface_hub import hf_hub_url\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" + "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": "4NSYVTaZiXs-" + "id": "R4574ETOHOSl" }, - "execution_count": 13, + "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ - "fetch_issues()" + "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": 66, + "height": 255, "referenced_widgets": [ - "1abf35d7e3f84665be946d3821684528", - "d48d4dae51db421d804cb579063c8049", - "1ea6e079d84446ef800bb11d1216ec15", - "1df3b19b5e4a4ca893d4984097e36d33", - "c853c3926d02463a85c74d6913ed7338", - "16c862053a824d3c87f5171a3bafc0ef", - "ef5483a5ab3c428996176b2fe48a7ab9", - "e2d31c986e5b4be88c655cebaf359872", - "92e840b0e057400ea43ae23dadb7e5d8", - "e57aaffa10c445ae99238b0d8a253f4f", - "cefe388fbd784838a975e7dda1984c94" + "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": "eE9KLjNAkYn3", - "outputId": "472503dc-6cd1-4eac-b05f-cec245c1648d" + "id": "h53H5uIDeIfM", + "outputId": "09e14018-acd0-47bb-f8b7-0fc314eda1cb" }, - "execution_count": 14, + "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": "1abf35d7e3f84665be946d3821684528", + "model_id": "03e6c647fe6c49c193e17923cd72213f", "version_minor": 0, "version_major": 2 }, "text/plain": [ - " 0%| | 0/100 [00:00 0)\n", + ")\n", + "issues_dataset" ], "metadata": { "colab": { - "base_uri": "https://localhost:8080/" + "base_uri": "https://localhost:8080/", + "height": 138, + "referenced_widgets": [ + "28fb2aace5f24254871e5b08e5dfee58", + "7da6b95d0aaf4bfc843d265c5a105644", + "bcbc95df2dbe443c86dca31423f1c831", + "fa3018954eef43008c439be3e9e43f31", + "b52cef6d99834adc82e2d922d671d5e5", + "07292b80756a4cbfb1ad5ecf29bb1ea6", + "8b4ce7eafd7c48b595896ce9deb28727", + "d71b3864e5cf4675b36b7033fbd21ffc", + "e1288966043645b8a0e11e9c1b50c75f", + "9718ce6bacaa47feb3e63ddebff37573", + "948f5cd3191644fca249520e8823dd8c" + ] }, - "id": "E50ktLWMgKcQ", - "outputId": "5ee3cfe1-ec87-42ac-af23-47745c3dc6ac" + "id": "EOkBuyZae3xk", + "outputId": "ee950bdf-1702-4b46-a55d-f072950c2af9" }, - "execution_count": 20, + "execution_count": null, "outputs": [ { - "output_type": "stream", - "name": "stderr", - "text": [ - "Loading cached shuffled indices for dataset at /root/.cache/huggingface/datasets/json/default-3884bd80e2aae1ff/0.0.0/c90812beea906fcffe0d5e3bb9eba909a80a998b5f88e9f8acbd320aa91acfde/cache-b8ded3760acae811.arrow\n" - ] + "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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