{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JMUZR2wzq1iw",
    "outputId": "40e6c137-60b2-4f09-a8d3-907bbacb000b"
   },
   "outputs": [],
   "source": [
    "!pip install datasets transformers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "eOqxutpwBZrF"
   },
   "outputs": [],
   "source": [
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "ANl4rkjCrz9y"
   },
   "outputs": [],
   "source": [
    "from datasets import load_dataset, load_metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "U4Sx1Is3qUKL"
   },
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 145,
     "referenced_widgets": [
      "5c0eeabe42f742d0837dc0a988f982f9",
      "800789760abe4fcaa188211954975831",
      "b18e92f368d6464cbbe101d3c5661ff6",
      "a8ddf9edd53b4883afd6d15657dbb9cd",
      "59210fb3d86b4cbcaa77431134886db0",
      "d14d8e143da049dab7d3519b529cd6fc",
      "d53511865d1e42bb9932742e30f393fb",
      "d5b44bceac6e472683f7242be2fe2991",
      "f2f6d59e2c334377be9707a5100c7196",
      "196f37fba1984589b97791690afd74b5",
      "94c770297adc430dbdf4cbd9dda8fe6a",
      "4f09c48e137c4628befe4369c9ce9f80",
      "9e54901f8e6d4f25af3533341ebfccaa",
      "38b51a53d35946fcb66ce58d0568e183",
      "b08d24b0a03a4e8aba7eadce85129627",
      "db4bf464446a496d9239464707a76b7a",
      "367ff8fc36494ba99c7d78b3a6812aca",
      "4a51363a375e474dbb9d31f0496beb5f",
      "8ada0f7ed6d34115808b30cc4e0ef4e8",
      "b7fe4fc7ca5a4127a541256bb683747e",
      "9d1925afb7b34bef9220604becd66192",
      "3760c715774e45699b552375951a5b32",
      "ab04f980a76643dc8f5dc2d2ceb49ef2",
      "015abd2f0c334ec1ba56d403c969d9f7",
      "b1faf1e1f1f94de4868b2136669dab3e",
      "a82eddb5d31746b9b6cf90336d25b3cf",
      "a06652061f4c42b0aa499724c8c7c8da",
      "e308afca22bc4339a60e859576592c59",
      "975d544b3fcb4e9c9181a14d11d6df48",
      "a296300ef30145f9873e57305e4d2290",
      "08c9ac8dbecf48d78fa423efe4573c6d",
      "dcf80e4357d349669aac7282993124f4",
      "0b135005b7594399b014f4074c0636a2",
      "fa3f16a495f54f54969ad0506d02ce94",
      "6df6be01f56f44f0aa5450253d084e85",
      "f3f35a9bac58437ca8263cafd4d15a8a",
      "1e17bee0af714291a8232f981e21b5b1",
      "aaebd289e8824fa7829736315af59b1b",
      "269a3b590fde456794e642ccb075b6b7",
      "ec6c8742376447a28019387797d2f004",
      "8f4edd79e04a42c78cb103666cec94d0",
      "fc9a928c74364e4f98dadb33274257a2",
      "63b056e91795491a93f412edb481ec56",
      "63ad91b30d5d47f9ba4157c829572125"
     ]
    },
    "id": "4MoYj2BhrNoR",
    "outputId": "827bbfb1-6866-48f7-e6f6-89dd1cb7569d"
   },
   "outputs": [],
   "source": [
    "tokenizer=AutoTokenizer.from_pretrained(\"ProsusAI/finbert\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "KVQWyhINwtsn"
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv('/content/news_prepared_to_model_wig20_th.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "xITSJh74j0Si",
    "outputId": "fe086784-b395-472f-faeb-68aa17666a7f"
   },
   "outputs": [],
   "source": [
    "df['sentiment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "f0vFNOVWw-8j"
   },
   "outputs": [],
   "source": [
    "df['sentiment']=df['sentiment'].map({1:'negative',2:'neutral',3:'positive'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "hcPFO0lI8JCK"
   },
   "outputs": [],
   "source": [
    "get_dum_labels=pd.get_dummies(df['sentiment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "a6GPKtQJ8W0E"
   },
   "outputs": [],
   "source": [
    "df=df.join(get_dum_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "rb2sPBE3582j"
   },
   "outputs": [],
   "source": [
    "df=df.rename(columns={'sentiment':'label'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "FZ_CMmBKF9eS"
   },
   "outputs": [],
   "source": [
    "df=df[['text','positive','negative','neutral']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "5ujVQEtPxEqU"
   },
   "outputs": [],
   "source": [
    "df.to_csv('/content/news_prepared.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153,
     "referenced_widgets": [
      "23c59c8e697342c3b931082f30f8808b",
      "8686297bdece4aaaba87a1a057f4529e",
      "0f9164db0472482796db1795b52b56dc",
      "db939de9eecd477bae0d067519104874",
      "1f8de36733b9486f93d113d6ca04d10c",
      "1517071a8f1749329b93cc1123b8840e",
      "1307b6ac1e4442a7bbd3f889ef60848c",
      "b49213b73a65433fa1dfd147fd6e665d",
      "3f37e2ebbc0e4035a597504605fb9a26",
      "74df003bf51b4c0e98ee8cb12deec27a",
      "d55aa4b89b9f473ba2d3ff56115b998f",
      "82da200245d64b73bc221601ed43037d",
      "a6a87348ab294dd5aac08dbf4c65d556",
      "a37bedd3808f45b29227b230fb00dfc7",
      "00f2dc4c98b34a35aaab9f34c58c7e6f",
      "8ccc230b1d3e4f60962597ae4bceb98b",
      "6ab792d79ec140d6a6a4f4826d49cb9a",
      "d0c2d8d39df445bf85fab1dfc8356508",
      "58f8d0816ce74be29660b64eede72020",
      "ed7c44675d8c4bf892f3b27c649c99f0",
      "734dd65c417b4de3924ee3e4938d3430",
      "4ae393b580af45988014f55a35303f89",
      "5695efb909b345c5b1dedb474f0516dd",
      "b44561a2242544b195a051facef9d528",
      "de90ea7248134d08b7112a3115f3e95b",
      "c7234223f26647e78fc0729c44c13f7f",
      "0fcbc4953edd4c9bb553319e5854a540",
      "02077263c2c847ef991858c222c30f0b",
      "f41c4cb9aa054c389baa91af4ed1e129",
      "fe8e5fb958ac42599f95aec6ae02e6e5",
      "6b3d7512d1894fc5a58cc20e526a8e02",
      "70f06b0c11b145efbd7545d2d1e07f61",
      "bea79b13877947b69f056b4a721bf0d3"
     ]
    },
    "id": "lXRzx2AJsYXC",
    "outputId": "5161f665-7086-4092-aeb2-65d65f080bc5"
   },
   "outputs": [],
   "source": [
    "from datasets import load_dataset, load_metric\n",
    "dataset=load_dataset('csv', data_files='/content/news_prepared.csv', split='train')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "ESK8hyfC0O7x"
   },
   "outputs": [],
   "source": [
    "dataset = dataset.train_test_split(test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 255,
     "referenced_widgets": [
      "2e83bfab28f24d6487952b01da679a3e",
      "3b365909caf845509830ad4e2ed75427",
      "2fee823b3a78472ea09856facf6b9a41",
      "3eedab7d4efb42a6bb4694f11b21c92a",
      "9d016a17dc9d4f58a919d0a5e090a63b",
      "3aca98a64e3e448aaeffc84babf4534a",
      "fd0e8c9fed11409fa9a667669498833a",
      "02483bf9d91d4f77bd1c053b3f6608aa",
      "75779cfb7a7f46079cbbbd8203892eb8",
      "1122113f7bc042489baa517d5427e723",
      "344e13dcf7ce4c7e8107b822b4da1991",
      "e394c883e1524b81b7ec49faa8dc669d",
      "12491831d4fd4ff29eb0b270b4d25ce6",
      "93d52bc97173458a9bae4a0c622de11b",
      "7a584934ebec43b0bde080c7298292d8",
      "c2ce447ecd0b4a5cbe78d70e864d667f",
      "1b0c42041d90468ea5dfce1f6b1e27ae",
      "4ca89c5fced142bdacc506598717b365",
      "6085b61a67ef4c0fb113324c8e9ad0b7",
      "c7ddde08a10a4399a31cf30ac4bd24bb",
      "1422d1da3aa7481a815c3f984ff6a2ec",
      "5987a80acc6045d2b141c2225fc6c8b4"
     ]
    },
    "id": "cQz0FnpF9MTs",
    "outputId": "0c27d129-ebf3-4e87-ced8-5064246a8e7b"
   },
   "outputs": [],
   "source": [
    "cols = dataset['train'].column_names\n",
    "\n",
    "dataset = dataset.map(lambda x : {\"label\": [x[c] for c in cols if c != \"text\"]})\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "bpSn3KkV0adJ"
   },
   "outputs": [],
   "source": [
    "def preprocess(data):\n",
    "    return tokenizer(data['text'], padding=True, truncation=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
     "referenced_widgets": [
      "20e6cdbda2b149b788751ee0f66bc18c",
      "93ad377446974d7e9b23b1a9f825f101",
      "6fb09bd76d5644d3ad4be34ba221cd73",
      "26f999167dca493d9afd40c48552cee1",
      "a021c4029252490681931bc088248876",
      "4a9581c0f2d64d5d9de1a5e8f9f28b7d",
      "269dd45c3a304f4f94f8da2f968251cd",
      "225cc413d59a49e3be035578bf3641ed",
      "43507303f08b4eaca9f83563b7df088d",
      "32feec8f1c6d4c7290e67a6a7630a72e",
      "3c943841807a4e768b07c483b8a18940",
      "739e36f2bfd24cd780a2a4e18d6e3b2b",
      "cdc2db236bd941349bbb9081b7d63ff6",
      "5bd9667a404342e4a905831007945adb",
      "70cb693232274c80bcdc654484128610",
      "8f9f59dfe0f84e6fab9d2389ac833be5",
      "d771ef94262e4657b827076ebb05ae79",
      "6f63f4dd455e47e89d07cde7125a17ba",
      "c98553e25e504580a63cf27785e8ce58",
      "281d5fafc55e46f5bd22f64f251cf6ca",
      "429d51930b604148a12610741458e128",
      "2275a83460b64a878e94c6620c21f6f1"
     ]
    },
    "id": "zwtE__S2g0d6",
    "outputId": "a229394f-fac1-4413-e6a8-4b38e0086902"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "20e6cdbda2b149b788751ee0f66bc18c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2593 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "739e36f2bfd24cd780a2a4e18d6e3b2b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1112 [00:00<?, ?ba/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset=dataset.map(preprocess, batched=True, batch_size=len(dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OpFFHH34hU45",
    "outputId": "aa3c1b4f-e19b-4fdc-ace4-6e5e17ef4e49"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['text', 'positive', 'negative', 'neutral', 'label', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
       "        num_rows: 5186\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['text', 'positive', 'negative', 'neutral', 'label', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
       "        num_rows: 2223\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LG7QItbk_44a",
    "outputId": "8b8fe28b-9dab-4229-d8ea-23c52d9232e9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['text', 'positive', 'negative', 'neutral', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
       "    num_rows: 2223\n",
       "})"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_end_train=dataset['train']['label']\n",
    "label_end_test=dataset['test']['label']\n",
    "dataset['train'].remove_columns('label')\n",
    "dataset['test'].remove_columns('label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "apj_J2UAvkuZ"
   },
   "outputs": [],
   "source": [
    "dataset['train']=dataset['train'].remove_columns('label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "U8j4numYlnFU"
   },
   "outputs": [],
   "source": [
    "dataset['test']=dataset['test'].remove_columns('label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Wm1PESzh0zOF",
    "outputId": "72964509-164a-4ea9-c067-6a32daf564fb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dataset({\n",
       "    features: ['text', 'positive', 'negative', 'neutral', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
       "    num_rows: 2223\n",
       "})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "JlsIX0FqjUx-"
   },
   "outputs": [],
   "source": [
    "dataset['train']=dataset['train'].add_column(name='label', column=label_end_train)\n",
    "dataset['test']=dataset['test'].add_column(name='label', column=label_end_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FXKfGXam04_r",
    "outputId": "361b3dbc-0778-423f-d812-44a98b1062ca"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['text', 'positive', 'negative', 'neutral', 'input_ids', 'token_type_ids', 'attention_mask', 'label'],\n",
       "        num_rows: 5186\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['text', 'positive', 'negative', 'neutral', 'input_ids', 'token_type_ids', 'attention_mask', 'label'],\n",
       "        num_rows: 2223\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "Zzi3On6kOejf"
   },
   "outputs": [],
   "source": [
    "dataset['train']=dataset['train'].remove_columns('text')\n",
    "dataset['test']=dataset['test'].remove_columns('text')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 81,
     "referenced_widgets": [
      "b066ad68c7534d46a6b4e4b75222c0c7",
      "3488c16fe96c44b89f95e5c0be68fbe0",
      "b2505b4113de450d9db82d644435c519",
      "927d172f2c8c4c608153642c894996d4",
      "c2f1c59f473742b686f4406669d16be8",
      "f3c95ce16afc440fa868620d2f9f68a1",
      "736c287264e0451e885ee78308a5300f",
      "a554297b149b459ab25761b185e3fdad",
      "f0d88ea28c4945e4bb515d1aa62b51bb",
      "6ad66904592c4ffb8526abe9e3b5e37e",
      "5b35983fcfa7472e9c41023709084c07",
      "46629f3d821c476aa9c61e8e56907045",
      "51a1a267c8224009a1f2bcc09255f090",
      "5b09789c82e54d71b0f2399c5ab3ebb4",
      "6ccfde05855746b4baaa5ad70716a425",
      "b9d626e21b044035ac91e157258580ca",
      "5adfb29048084aad9eac7e30f3dc6ce8",
      "f9b65ac9e4444a6fa64f636e2082da0e",
      "b92385ce0eb44972bf51e070f70e3d60",
      "92fd0f4baa934016a061834bca51f049",
      "3078baa7d54c4134bc8194ca3de80ead",
      "8957af3885ed4074aaeb4525b6b0ee8b"
     ]
    },
    "id": "NwYDr_0VA5k0",
    "outputId": "62f0aaba-a7d9-45b6-fa85-70c1083cb0e8"
   },
   "outputs": [],
   "source": [
    "#cast labels to float\n",
    "dataset.set_format(\"torch\")\n",
    "dataset = (dataset\n",
    "          .map(lambda x : {\"float_labels\": x[\"label\"].to(torch.float)}, remove_columns=[\"label\"])\n",
    "          .rename_column(\"float_labels\", \"label\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "YkXfXNarR5aA"
   },
   "outputs": [],
   "source": [
    "dataset=dataset.remove_columns(['positive','negative','neutral'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7ja5F_JKSCfa",
    "outputId": "030a3cba-ed97-4dc2-8084-0ce688c6c449"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'input_ids': tensor([  101,  6948,  2361, 18668,  5630,  2000,  2035, 24755,  2618,  1037,\n",
       "         11443,  4859,  1997, 20228,  2078,  2454,  1045,  1041, 20228,  2078,\n",
       "          2566,  3745,  2004,  2012,  1996,  2154,  1997, 11092,  1037,  5813,\n",
       "          2030, 20228,  2078,  2044,  2825,  7375,  1997, 10943,  2077,  1996,\n",
       "          2154,   102]),\n",
       " 'token_type_ids': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "         0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
       " 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),\n",
       " 'label': tensor([1., 0., 0.])}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['train'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "zaw0H3CQ12yJ"
   },
   "outputs": [],
   "source": [
    "batch_size=16\n",
    "epochs=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "mScEpfPje8Ov"
   },
   "outputs": [],
   "source": [
    "#!pip install evaluate\n",
    "import evaluate\n",
    "import numpy as np\n",
    "def compute_metrics(eval_pred):\n",
    "  accuracy_score=evaluate.load('accuracy')\n",
    "  predictions, labels = eval_pred\n",
    "  predictions=np.argmax(predictions, axis=-1)\n",
    "  labels=np.argmax(labels, axis=-1)\n",
    "  \n",
    "  return accuracy_score.compute(references=labels, predictions=predictions)\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "arMQT9Du1cMo",
    "outputId": "51aa1342-1fdb-4071-9340-c23d5b0db007"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "PyTorch: setting up devices\n",
      "The default value for the training argument `--report_to` will change in v5 (from all installed integrations to none). In v5, you will need to use `--report_to all` to get the same behavior as now. You should start updating your code and make this info disappear :-).\n"
     ]
    }
   ],
   "source": [
    "args = TrainingArguments(\n",
    "    evaluation_strategy = \"epoch\",\n",
    "    save_strategy = \"epoch\",\n",
    "    learning_rate=2e-5,\n",
    "    per_device_train_batch_size=batch_size,\n",
    "    per_device_eval_batch_size=batch_size,\n",
    "    num_train_epochs=epochs,\n",
    "    weight_decay=0.01,\n",
    "    load_best_model_at_end=True,\n",
    "    metric_for_best_model='accuracy',\n",
    "    push_to_hub=True,\n",
    "    output_dir='/content/stock_sentiment_hp'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "ubVDrsZi2rW-"
   },
   "outputs": [],
   "source": [
    "#according to docs we have to wrap model in function\n",
    "def model_init():\n",
    "    return AutoModelForSequenceClassification.from_pretrained(\"ProsusAI/finbert\", num_labels=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "i7gWwrHh2s-1",
    "outputId": "7d1e051a-5896-4d0f-bd09-a458adbc4485"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "loading configuration file config.json from cache at /root/.cache/huggingface/hub/models--ProsusAI--finbert/snapshots/54bddcea2cca580dd1df6a88d33242dcf4c61a71/config.json\n",
      "Model config BertConfig {\n",
      "  \"_name_or_path\": \"ProsusAI/finbert\",\n",
      "  \"architectures\": [\n",
      "    \"BertForSequenceClassification\"\n",
      "  ],\n",
      "  \"attention_probs_dropout_prob\": 0.1,\n",
      "  \"classifier_dropout\": null,\n",
      "  \"gradient_checkpointing\": false,\n",
      "  \"hidden_act\": \"gelu\",\n",
      "  \"hidden_dropout_prob\": 0.1,\n",
      "  \"hidden_size\": 768,\n",
      "  \"id2label\": {\n",
      "    \"0\": \"positive\",\n",
      "    \"1\": \"negative\",\n",
      "    \"2\": \"neutral\"\n",
      "  },\n",
      "  \"initializer_range\": 0.02,\n",
      "  \"intermediate_size\": 3072,\n",
      "  \"label2id\": {\n",
      "    \"negative\": 1,\n",
      "    \"neutral\": 2,\n",
      "    \"positive\": 0\n",
      "  },\n",
      "  \"layer_norm_eps\": 1e-12,\n",
      "  \"max_position_embeddings\": 512,\n",
      "  \"model_type\": \"bert\",\n",
      "  \"num_attention_heads\": 12,\n",
      "  \"num_hidden_layers\": 12,\n",
      "  \"pad_token_id\": 0,\n",
      "  \"position_embedding_type\": \"absolute\",\n",
      "  \"transformers_version\": \"4.24.0\",\n",
      "  \"type_vocab_size\": 2,\n",
      "  \"use_cache\": true,\n",
      "  \"vocab_size\": 30522\n",
      "}\n",
      "\n",
      "loading weights file pytorch_model.bin from cache at /root/.cache/huggingface/hub/models--ProsusAI--finbert/snapshots/54bddcea2cca580dd1df6a88d33242dcf4c61a71/pytorch_model.bin\n",
      "All model checkpoint weights were used when initializing BertForSequenceClassification.\n",
      "\n",
      "All the weights of BertForSequenceClassification were initialized from the model checkpoint at ProsusAI/finbert.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use BertForSequenceClassification for predictions without further training.\n",
      "/content/stock_sentiment_hp is already a clone of https://huggingface.co/slisowski/stock_sentiment_hp. Make sure you pull the latest changes with `repo.git_pull()`.\n",
      "WARNING:huggingface_hub.repository:/content/stock_sentiment_hp is already a clone of https://huggingface.co/slisowski/stock_sentiment_hp. Make sure you pull the latest changes with `repo.git_pull()`.\n"
     ]
    }
   ],
   "source": [
    "trainer = Trainer(\n",
    "    model_init=model_init,\n",
    "    args=args,\n",
    "    train_dataset=dataset['train'],\n",
    "    eval_dataset=dataset['test'],\n",
    "    tokenizer=tokenizer,\n",
    "    compute_metrics=compute_metrics\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mQ2NxICNbXqz",
    "outputId": "d96644ca-71cb-4c66-927f-2f11bb422df7"
   },
   "outputs": [],
   "source": [
    "#library to search hyperparameters\n",
    "!pip install \"ray[tune]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "PPhWI-iM3Z9f",
    "outputId": "c2db6e39-53a1-4723-a4d0-4118fe675f78"
   },
   "outputs": [],
   "source": [
    "#run hyperparameters tuning\n",
    "best_run = trainer.hyperparameter_search(n_trials=10, direction=\"maximize\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "uUMxS2n6EvFR",
    "outputId": "945ccf16-9602-404c-e761-3d42fd0f163c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "learning_rate 1.1207606211860571e-05\n",
      "num_train_epochs 4\n",
      "seed 2\n",
      "per_device_train_batch_size 16\n"
     ]
    }
   ],
   "source": [
    "#best hyperparameters\n",
    "for n, v in best_run.hyperparameters.items():\n",
    "    \n",
    "    if n=='seed':\n",
    "      v=int(np.round(v))\n",
    "    print(n,v)\n",
    "    setattr(trainer.args, n, v)"
   ]
  }
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