{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "7ee8d536", "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "execution": { "iopub.execute_input": "2024-09-30T00:34:44.859532Z", "iopub.status.busy": "2024-09-30T00:34:44.859168Z", "iopub.status.idle": "2024-09-30T00:35:19.005701Z", "shell.execute_reply": "2024-09-30T00:35:19.004768Z" }, "papermill": { "duration": 34.159766, "end_time": "2024-09-30T00:35:19.012080", "exception": false, "start_time": "2024-09-30T00:34:44.852314", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\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.\r\n", "cudf 24.8.3 requires cubinlinker, which is not installed.\r\n", "cudf 24.8.3 requires cupy-cuda11x>=12.0.0, which is not installed.\r\n", "cudf 24.8.3 requires ptxcompiler, which is not installed.\r\n", "cuml 24.8.0 requires cupy-cuda11x>=12.0.0, which is not installed.\r\n", "dask-cudf 24.8.3 requires cupy-cuda11x>=12.0.0, which is not installed.\r\n", "apache-beam 2.46.0 requires cloudpickle~=2.2.1, but you have cloudpickle 3.0.0 which is incompatible.\r\n", "apache-beam 2.46.0 requires dill<0.3.2,>=0.3.1.1, but you have dill 0.3.8 which is incompatible.\r\n", "apache-beam 2.46.0 requires numpy<1.25.0,>=1.14.3, but you have numpy 1.26.4 which is incompatible.\r\n", "apache-beam 2.46.0 requires pyarrow<10.0.0,>=3.0.0, but you have pyarrow 17.0.0 which is incompatible.\r\n", "beatrix-jupyterlab 2024.66.154055 requires jupyterlab~=3.6.0, but you have jupyterlab 4.2.5 which is incompatible.\r\n", "cudf 24.8.3 requires cuda-python<12.0a0,>=11.7.1, but you have cuda-python 12.6.0 which is incompatible.\r\n", "cudf 24.8.3 requires pyarrow<16.2.0a0,>=16.1.0, but you have pyarrow 17.0.0 which is incompatible.\r\n", "ibis-framework 7.1.0 requires pyarrow<15,>=2, but you have pyarrow 17.0.0 which is incompatible.\r\n", "rapids-dask-dependency 24.8.0a0 requires dask==2024.7.1, but you have dask 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\r\n", "\u001b[0m" ] }, { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "! pip install requests pyarrow datasets duckdb -qU\n", "import os, time, sys, requests, torch, glob\n", "from pathlib import Path\n", "import pandas as pd\n", "import duckdb as ddg\n", "import numpy as np\n", "import pyarrow.parquet as pq\n", "from IPython.display import display, HTML, clear_output\n", "from transformers import GPT2Tokenizer, GPT2LMHeadModel\n", "from datasets import Dataset\n", "from os.path import basename as osb\n", "from huggingface_hub import HfApi, create_repo\n", "\n", "from kaggle_secrets import UserSecretsClient\n", "user_secrets = UserSecretsClient()\n", "email = user_secrets.get_secret(\"dtdfw_email\")\n", "name = user_secrets.get_secret(\"mysecretname\")\n", "\n", "\n", "import wandb\n", "wandb.init(mode='disabled')" ] }, { "cell_type": "code", "execution_count": 2, "id": "73a8b020", "metadata": { "execution": { "iopub.execute_input": "2024-09-30T00:35:19.023890Z", "iopub.status.busy": "2024-09-30T00:35:19.023582Z", "iopub.status.idle": "2024-09-30T00:35:19.028799Z", "shell.execute_reply": "2024-09-30T00:35:19.028057Z" }, "papermill": { "duration": 0.013125, "end_time": "2024-09-30T00:35:19.030637", "exception": false, "start_time": "2024-09-30T00:35:19.017512", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "work = '/kaggle/working/'\n", "\n", "dataset_location = 'https://huggingface.co/datasets/DenyTranDFW/SEC_10K_FSNoNDS_Zip/'\n", "\n", "rar_file_download = dataset_location + 'resolve/main/OFFICE_OF_TRADE_AND_SERVICES/RETAIL_EATING_PLACES.rar'\n", "basename = osb(rar_file_download)\n", "rar_file_downloaded = work + basename\n", "rar_file_unrar = work + basename.split('.')[0]\n", "\n", "output_csv = work + 'out_csv'\n", "os.makedirs(output_csv, exist_ok=True)" ] }, { "cell_type": "code", "execution_count": 3, "id": "98ac006c", "metadata": { "execution": { "iopub.execute_input": "2024-09-30T00:35:19.041990Z", "iopub.status.busy": "2024-09-30T00:35:19.041699Z", "iopub.status.idle": "2024-09-30T00:35:27.513097Z", "shell.execute_reply": "2024-09-30T00:35:27.512171Z" }, "papermill": { "duration": 8.479446, "end_time": "2024-09-30T00:35:27.515198", "exception": false, "start_time": "2024-09-30T00:35:19.035752", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "!cd {work}\n", "!sudo apt install libarchive-tools\n", "!wget {rar_file_download} \n", "clear_output()" ] }, { "cell_type": "code", "execution_count": 4, "id": "dc016bad", "metadata": { "execution": { "iopub.execute_input": "2024-09-30T00:35:27.527593Z", "iopub.status.busy": "2024-09-30T00:35:27.527237Z", "iopub.status.idle": "2024-09-30T00:35:28.784020Z", "shell.execute_reply": "2024-09-30T00:35:28.782945Z" }, "papermill": { "duration": 1.26547, "end_time": "2024-09-30T00:35:28.786374", "exception": false, "start_time": "2024-09-30T00:35:27.520904", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "!bsdtar -xf {rar_file_downloaded}" ] }, { "cell_type": "code", "execution_count": 5, "id": "6ac1755f", "metadata": { "execution": { "iopub.execute_input": "2024-09-30T00:35:28.798775Z", "iopub.status.busy": "2024-09-30T00:35:28.798428Z", "iopub.status.idle": "2024-09-30T00:35:28.822992Z", "shell.execute_reply": "2024-09-30T00:35:28.822295Z" }, "papermill": { "duration": 0.0331, "end_time": "2024-09-30T00:35:28.825020", "exception": false, "start_time": "2024-09-30T00:35:28.791920", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "duckdb = 'duck.db'\n", "duck = ddg.connect(duckdb)" ] }, { "cell_type": "code", "execution_count": 6, "id": "d5cb6232", "metadata": { "execution": { "iopub.execute_input": "2024-09-30T00:35:28.837429Z", "iopub.status.busy": "2024-09-30T00:35:28.837110Z", "iopub.status.idle": "2024-09-30T00:35:28.853514Z", "shell.execute_reply": "2024-09-30T00:35:28.852634Z" }, "papermill": { "duration": 0.024186, "end_time": "2024-09-30T00:35:28.855374", "exception": false, "start_time": "2024-09-30T00:35:28.831188", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "duck.execute(f'DROP SEQUENCE IF EXISTS question_id_seq;')\n", "duck.execute(\"CREATE SEQUENCE IF NOT EXISTS id_seq;\")\n", "\n", "duck.execute(f'''\n", "CREATE TABLE _data (\n", " id INT DEFAULT nextval('id_seq'),\n", " tag VARCHAR,\n", " ddate INT,\n", " line INT,\n", " PRIMARY KEY (id)\n", ");''')" ] }, { "cell_type": "code", "execution_count": 7, "id": "60640d90", "metadata": { "execution": { "iopub.execute_input": "2024-09-30T00:35:28.867185Z", "iopub.status.busy": "2024-09-30T00:35:28.866915Z", "iopub.status.idle": "2024-09-30T00:35:28.915334Z", "shell.execute_reply": "2024-09-30T00:35:28.914749Z" }, "papermill": { "duration": 0.056553, "end_time": "2024-09-30T00:35:28.917212", "exception": false, "start_time": "2024-09-30T00:35:28.860659", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "parquet_files = [str(x) for x in Path(rar_file_unrar).glob(\"**/*.parquet\")]" ] }, { "cell_type": "code", "execution_count": 8, "id": "7e8ba4bb", "metadata": { "execution": { "iopub.execute_input": "2024-09-30T00:35:28.929181Z", "iopub.status.busy": "2024-09-30T00:35:28.928884Z", "iopub.status.idle": "2024-09-30T00:35:43.752804Z", "shell.execute_reply": "2024-09-30T00:35:43.751900Z" }, "papermill": { "duration": 14.832616, "end_time": "2024-09-30T00:35:43.755273", "exception": false, "start_time": "2024-09-30T00:35:28.922657", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "for file in parquet_files:\n", " df = pd.read_parquet(file).sort_values(by=['ddate', 'line']) \n", " duck.execute(f\"INSERT INTO _data SELECT NEXTVAL('id_seq') AS id, tag, ddate, line FROM df;\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "3c6c7e9f", "metadata": { "execution": { "iopub.execute_input": "2024-09-30T00:35:43.768780Z", "iopub.status.busy": "2024-09-30T00:35:43.768066Z", "iopub.status.idle": "2024-09-30T00:35:43.883044Z", "shell.execute_reply": "2024-09-30T00:35:43.882248Z" }, "papermill": { "duration": 0.123868, "end_time": "2024-09-30T00:35:43.885252", "exception": false, "start_time": "2024-09-30T00:35:43.761384", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "text_data = ' '.join([i[0] for i in list(duck.execute(f\"SELECT tag FROM _data ORDER BY id;\").df().to_numpy())])" ] }, { "cell_type": "code", "execution_count": 10, "id": "6a165e02", "metadata": { "execution": { "iopub.execute_input": "2024-09-30T00:35:43.897857Z", "iopub.status.busy": "2024-09-30T00:35:43.897146Z", "iopub.status.idle": "2024-09-30T00:35:56.028944Z", "shell.execute_reply": "2024-09-30T00:35:56.028151Z" }, "papermill": { "duration": 12.140318, "end_time": "2024-09-30T00:35:56.031246", "exception": false, "start_time": "2024-09-30T00:35:43.890928", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "from transformers import Trainer, TrainingArguments" ] }, { "cell_type": "code", "execution_count": 11, "id": "34a13713", "metadata": { "execution": { "iopub.execute_input": "2024-09-30T00:35:56.043790Z", "iopub.status.busy": "2024-09-30T00:35:56.043170Z", "iopub.status.idle": "2024-09-30T00:35:56.466834Z", "shell.execute_reply": "2024-09-30T00:35:56.465487Z" }, "papermill": { "duration": 0.432533, "end_time": "2024-09-30T00:35:56.469426", "exception": false, "start_time": "2024-09-30T00:35:56.036893", "status": "completed" }, "tags": [] }, "outputs": [], "source": [ "tags = text_data.split()\n", "\n", "sequence_length = 50\n", "\n", "def create_sequences(tags, seq_length):\n", " sequences = []\n", " for i in range(0, len(tags) - seq_length):\n", " seq = ' '.join(tags[i:i + seq_length])\n", " sequences.append(seq)\n", " return sequences\n", "\n", "sequences = create_sequences(tags, sequence_length)" ] }, { "cell_type": "code", "execution_count": 12, "id": "1d168561", "metadata": { "execution": { "iopub.execute_input": "2024-09-30T00:35:56.493524Z", "iopub.status.busy": "2024-09-30T00:35:56.492317Z", "iopub.status.idle": "2024-09-30T00:36:05.033794Z", "shell.execute_reply": "2024-09-30T00:36:05.032819Z" }, "papermill": { "duration": 8.555895, "end_time": "2024-09-30T00:36:05.035974", "exception": false, "start_time": "2024-09-30T00:35:56.480079", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fb2b3d2a53a643dc9074e2e0f4dae800", "version_major": 2, "version_minor": 0 }, "text/plain": [ "tokenizer_config.json: 0%| | 0.00/26.0 [00:00\n", " \n", " \n", " [15251/15251 3:31:31, Epoch 1/1]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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StepTraining Loss
5000.579000
10000.289900
15000.227400
20000.193600
25000.168900
30000.153600
35000.137700
40000.126500
45000.116600
50000.109700
55000.104000
60000.098000
65000.093900
70000.089000
75000.086300
80000.081600
85000.080500
90000.076900
95000.075300
100000.073100
105000.071600
110000.069600
115000.067800
120000.067600
125000.065400
130000.064700
135000.064000
140000.063600
145000.063200
150000.062400

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.10/site-packages/torch/nn/parallel/parallel_apply.py:79: FutureWarning: `torch.cuda.amp.autocast(args...)` is deprecated. Please use `torch.amp.autocast('cuda', args...)` instead.\n", " with torch.cuda.device(device), torch.cuda.stream(stream), autocast(enabled=autocast_enabled):\n", "/opt/conda/lib/python3.10/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n", " warnings.warn('Was asked to gather along dimension 0, but all '\n" ] }, { "data": { "text/plain": [ "TrainOutput(global_step=15251, training_loss=0.11975346933497061, metrics={'train_runtime': 12693.6809, 'train_samples_per_second': 9.611, 'train_steps_per_second': 1.201, 'total_flos': 3.1878673072128e+16, 'train_loss': 0.11975346933497061, 'epoch': 1.0})" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trainer.train()" ] }, { "cell_type": "code", "execution_count": 17, "id": "5337070c", "metadata": { "execution": { "iopub.execute_input": "2024-09-30T04:13:27.669337Z", 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