{ "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)" ] } ], "metadata": { "accelerator": "GPU", "colab": { "provenance": [] }, "gpuClass": "premium", "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.6" }, "widgets": { 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