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"\u001b[?25hCollecting yarl<2.0,>=1.0\n",
" Downloading yarl-1.9.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (269 kB)\n",
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"\u001b[?25hRequirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub<1.0.0,>=0.11.0->datasets) (4.5.0)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests>=2.19.0->datasets) (2022.12.7)\n",
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"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests>=2.19.0->datasets) (3.4)\n",
"Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas->datasets) (2022.7.1)\n",
"Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas->datasets) (2.8.2)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
"Installing collected packages: tokenizers, sentencepiece, xxhash, protobuf, multidict, frozenlist, dill, async-timeout, yarl, responses, multiprocess, huggingface-hub, aiosignal, transformers, aiohttp, datasets, evaluate\n",
" Attempting uninstall: protobuf\n",
" Found existing installation: protobuf 3.20.3\n",
" Uninstalling protobuf-3.20.3:\n",
" Successfully uninstalled protobuf-3.20.3\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"tensorflow 2.12.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.20.2 which is incompatible.\n",
"tensorflow-metadata 1.13.1 requires protobuf<5,>=3.20.3, but you have protobuf 3.20.2 which is incompatible.\u001b[0m\u001b[31m\n",
"\u001b[0mSuccessfully installed aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 datasets-2.11.0 dill-0.3.6 evaluate-0.4.0 frozenlist-1.3.3 huggingface-hub-0.13.4 multidict-6.0.4 multiprocess-0.70.14 protobuf-3.20.2 responses-0.18.0 sentencepiece-0.1.98 tokenizers-0.13.3 transformers-4.28.1 xxhash-3.2.0 yarl-1.9.1\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Авторизация Hugging face с помощью токена"
],
"metadata": {
"id": "PItOQrVMTxVo"
}
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 358,
"referenced_widgets": [
"44cf25be608847f0a31c8302c4e3480f",
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"33b73b25f5f4440195c1180d4c4d2a7c",
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"ba11f1eba99c4c4ebf91f029863f1113",
"6f79f7e3d97e4eb195f3601a80876926",
"90e7f8215fdc4787a7a70cd03f3dfd30",
"80eeed15d105410a81bf000ba9a97d9d",
"5d293a3508354775b526a80fcf92bdab",
"c56c52a362e349b39d3057c270a1b3cf",
"682ee3b686ed459eb42a88e5fa68b9df",
"a9b670d2afc74e17aed672c6ed7d9b10"
]
},
"id": "6GEIYtIjv9Xh",
"outputId": "89cae825-a5da-4ef2-ef6f-adf2f375c3ca"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Token is valid.\n",
"Your token has been saved in your configured git credential helpers (store).\n",
"Your token has been saved to /root/.cache/huggingface/token\n",
"Login successful\n"
]
}
],
"source": [
"from huggingface_hub import notebook_login\n",
"\n",
"notebook_login()"
]
},
{
"cell_type": "code",
"source": [
"from transformers import pipeline"
],
"metadata": {
"id": "Yr_tD5s8wnOg"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Загрузка модели из Hugging face"
],
"metadata": {
"id": "EN2MEPBEVRml"
}
},
{
"cell_type": "code",
"source": [
"model_checkpoint2 = \"Stacy123/rubert_tiny_qa_kontur\"\n",
"question_answerer = pipeline(\"question-answering\", model=model_checkpoint2)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 209,
"referenced_widgets": [
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},
"id": "g5Ctxzl1wqrC",
"outputId": "5597ce34-945a-4c1e-ddfa-503d6e2c030a"
},
"execution_count": 4,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)lve/main/config.json: 0%| | 0.00/699 [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "14e421a64a514b26b2a6a233ddac9f07"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading pytorch_model.bin: 0%| | 0.00/116M [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "8fae1b70c30b4927a7257e2cfb7107af"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)okenizer_config.json: 0%| | 0.00/368 [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "f8593cb455b54d4cae1ea0c8bec3213f"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)solve/main/vocab.txt: 0%| | 0.00/1.08M [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "3ba3b44fa7084f0db601407f94cb4841"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)/main/tokenizer.json: 0%| | 0.00/2.41M [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "b9ba616e75784fe18868eddc2754297a"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
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"Downloading (…)cial_tokens_map.json: 0%| | 0.00/125 [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "f6f1657a7f2c46b685f29508a40eb61f"
}
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"Загрузка и обработка тестовых данных"
],
"metadata": {
"id": "GZTwtBNBVV8R"
}
},
{
"cell_type": "code",
"source": [
"import json\n",
"with open('test.json', 'r') as f:\n",
" data = json.load(f)"
],
"metadata": {
"id": "Gg1PrMrC_gqQ"
},
"execution_count": 56,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def prep_data(dataset):\n",
" ids = []\n",
" questions = []\n",
" contexts = []\n",
" for i in range(len(dataset)):\n",
" ids.append(dataset[i]['id'])\n",
" questions.append(dataset[i]['label'])\n",
" contexts.append(dataset[i]['text'])\n",
" return {\n",
" 'id': ids,\n",
" 'question': questions,\n",
" 'context': contexts,\n",
" }"
],
"metadata": {
"id": "ffbJd_C7_ypw"
},
"execution_count": 57,
"outputs": []
},
{
"cell_type": "code",
"source": [
"dataset = prep_data(data)"
],
"metadata": {
"id": "ig4qLwm0_9UT"
},
"execution_count": 58,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import pandas as pd"
],
"metadata": {
"id": "qCkrqqCJABIo"
},
"execution_count": 59,
"outputs": []
},
{
"cell_type": "code",
"source": [
"cols = ['id', 'context','question','answers']\n",
"\n",
"data_df = pd.DataFrame(dataset, columns=cols) "
],
"metadata": {
"id": "QfO784cLAC-4"
},
"execution_count": 60,
"outputs": []
},
{
"cell_type": "code",
"source": [
"data_df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "FSSwpS3oAC2Q",
"outputId": "8752812f-597e-4797-f3be-1970a8d763a2"
},
"execution_count": 61,
"outputs": [
{
"output_type": "execute_result",
"data": {
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]
},
{
"cell_type": "code",
"source": [
"len(data_df)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "lIIQ9evrp2PU",
"outputId": "28c30e77-33bb-417a-b5f5-76bf2f91fad3"
},
"execution_count": 62,
"outputs": [
{
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"data": {
"text/plain": [
"318"
]
},
"metadata": {},
"execution_count": 62
}
]
},
{
"cell_type": "markdown",
"source": [
"Проверка одного примера"
],
"metadata": {
"id": "HGvw2DcXhA1v"
}
},
{
"cell_type": "code",
"source": [
"data_df.iloc[0].question"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"id": "w2LxQOfZxIws",
"outputId": "71509680-43aa-439c-b1f5-3d59fdb66399"
},
"execution_count": 63,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'обеспечение исполнения контракта'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 63
}
]
},
{
"cell_type": "code",
"source": [
"context = data_df.iloc[0].context\n",
"question = data_df.iloc[0].question\n",
"question_answerer(question=question, context=context)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "Q6jAJOtxxYn5",
"outputId": "17315276-69e7-4bf8-a593-0a6cae01339d"
},
"execution_count": 64,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'score': 0.0013592320028692484,\n",
" 'start': 1283,\n",
" 'end': 1348,\n",
" 'answer': 'Размер обеспечения исполнения Контракта составляет _______ рублей'}"
]
},
"metadata": {},
"execution_count": 64
}
]
},
{
"cell_type": "markdown",
"source": [
"Поиск ответов по всему датафрейму"
],
"metadata": {
"id": "5iLzdcjkhF0d"
}
},
{
"cell_type": "code",
"source": [
"for i in range(len(data_df)):\n",
" answer = question_answerer(question=data_df['question'][i], context=data_df['context'][i])\n",
" del answer['score']\n",
" data_df['answers'][i] = answer"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "IkbcDkfDAHOA",
"outputId": "6fdd0002-6349-4cc4-cb9a-5769e88714b6"
},
"execution_count": 65,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
":4: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" data_df['answers'][i] = answer\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"data_df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "crwTvkGJgUvT",
"outputId": "4f8a2b1e-b340-47af-8bec-8431b6008f93"
},
"execution_count": 66,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" id context \\\n",
"0 762883279 МУНИЦИПАЛЬНЫЙ КОНТРАКТ № ______ на оказание ус... \n",
"1 311837655 Извещение о проведении электронного аукциона д... \n",
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"id": "C37O1xeVGCXk"
},
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{
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" data_df[\"extracted_part\"][i][\"text\"] = [data_df[\"extracted_part\"][i].pop(\"answer\")]\n",
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" data_df[\"extracted_part\"][i][\"answer_end\"] = [data_df[\"extracted_part\"][i].pop(\"end\")]"
],
"metadata": {
"id": "vGTiqQ-yGCM4"
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"execution_count": 68,
"outputs": []
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{
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"id": "8GC2FrIXJXQ3",
"outputId": "0b619428-b060-4ed4-cfb0-5341f9347c27"
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"execution_count": 69,
"outputs": [
{
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"data": {
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"source": [
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],
"metadata": {
"id": "rRgVyLbAVfiP"
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{
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"source": [
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},
"execution_count": 70,
"outputs": []
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{
"cell_type": "code",
"source": [
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"outputId": "3cc60899-5072-46a5-e3a7-54792ca8bdef"
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"outputs": [
{
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"data": {
"text/plain": [
"{'id': 762883279,\n",
" 'text': 'МУНИЦИПАЛЬНЫЙ КОНТРАКТ № ______ на оказание услуг по техническому обслуживанию и ремонту принтеров и многофункциональных устройств, заправке и восстановлению картриджей (идентификационный код закупки 223861800296886010100100590019511244) г. Ханты-Мансийск «___» ____________ 2022 г. (или) возмещения убытков причинённых Исполнителем убытков. 6. Обеспечение исполнения контракта 6.1. Исполнение контракта обеспечиваются предоставлением независимой гарантии, выданной банком и соответствующей требованиям Федерального закона от 05.04.2013 № 44-ФЗ «О контрактной системе в сфере закупок товаров, работ, услуг для обеспечения государственных и муниципальных нужд», или внесением денежных средств на указанный заказчиком счет, на котором в соответствии с законодательством Российской Федерации учитываются операции со средствами, поступающими заказчику. Способ обеспечения исполнения контракта, срок действия независимой гарантии определяются в соответствии с требованиями Федерального закона от 05.04.2013 № 44-ФЗ «О контрактной системе в сфере закупок товаров, работ, услуг для обеспечения государственных и муниципальных нужд» участником закупки, с которым заключается контракт, самостоятельно. 6.2. Обеспечение исполнения Контракта предоставляется Заказчику до заключения Контракта. Размер обеспечения исполнения Контракта составляет _______ рублей ____ копеек (5 процентов от цены контракта). Получатель: Комитет по финансам администрации Ханты-Мансийского района (Администрация Ханты-Мансийского района, л/сч 040.01.001.2) ИНН 8618002968 КПП 860101001 Банк плательщика: РКЦ Ханты-Мансийск//УФК по Ханты-Мансийскому автономному округу – Югре г. Ханты-Мансийск, ЕКС 40102810245370000007 БИК 007162163 Казначейский счет 03232643718290008700 В случае заключения контракта по результатам определения Исполнителя в соответствии с пунктом 1 части 1 статьи 30 Федерального закона от 05.04.2013 № 44-ФЗ «О контрактной системе в сфере закупок товаров, работ, услуг для обеспечения государственных и муниципальных нужд» размер обеспечения исполнения контракта, в том числе предоставляемого с учетом положений статьи 37 Федерального закона от 05.04.2013 № 44-ФЗ «О контрактной системе в сфере закупок товаров, работ, услуг для обеспечения государственных и муниципальных нужд», устанавливается от цены, по которой в соответствии с Федерального закона от 05.04.2013 № 44-ФЗ «О контрактной системе в сфере 13.Адреса места нахождения, банковские реквизиты и подписи Сторон ЗАКАЗЧИК: Администрация Ханты-Мансийского района Юридический адрес: 628011, Тюменская область, Ханты-Мансийский автономный округ - Югра, г. Ханты-Мансийск, ул. Гагарина, 214 Почтовый адрес: 628011,',\n",
" 'label': 'обеспечение исполнения контракта',\n",
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" 'answer_start': [1283],\n",
" 'answer_end': [1348]}}"
]
},
"metadata": {},
"execution_count": 71
}
]
},
{
"cell_type": "code",
"source": [
"with open('predictions.json', 'w') as f:\n",
" json.dump(data_dict, f, ensure_ascii=False, indent=2)"
],
"metadata": {
"id": "ZqoeF-14KGh6"
},
"execution_count": 72,
"outputs": []
}
]
}