{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "1bcd4735-038a-4364-90ed-6e58d8fa2dac", "metadata": {}, "outputs": [], "source": [ "import re\n", "import pandas as pd\n", "from datasets import Dataset\n", "from huggingface_hub import login" ] }, { "cell_type": "code", "execution_count": null, "id": "a23cc23d-90d6-431f-8057-67dbac509de2", "metadata": {}, "outputs": [], "source": [ "# add the credential helper so we can use\n", "# the library to push data to the hub later\n", "\n", "!git config --global credential.helper cache\n", "\n", "# login to the hub\n", "\n", "login(\n", " '',\n", " add_to_git_credential=True\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "a28d0034-ce6b-4a76-ab52-6050c0c74bfc", "metadata": {}, "outputs": [], "source": [ "with open('bad.rpy', 'r') as f:\n", " rpy_text = f.read()" ] }, { "cell_type": "code", "execution_count": null, "id": "95397968-6caf-41b9-a27f-c806ac0a1993", "metadata": {}, "outputs": [], "source": [ "lines = re.findall(r\"^\\s{4}.*$\", rpy_text, re.MULTILINE) # Find all lines starting with 4 empty spaces" ] }, { "cell_type": "code", "execution_count": null, "id": "4558c31b-1b1d-4bfb-a76a-b36a09edaab3", "metadata": {}, "outputs": [], "source": [ "non_latin_lines = [line for line in lines if re.search(r\"[^\\x00-\\x7F]\", line)] # Get all lines containing non-latin characters" ] }, { "cell_type": "code", "execution_count": null, "id": "4c6d06de-aa42-4d52-bc82-8f469100769c", "metadata": {}, "outputs": [], "source": [ "latin_lines = [line for line in lines if line not in non_latin_lines] # Get only the lines containing latin characters" ] }, { "cell_type": "code", "execution_count": null, "id": "ef9eb581-b849-4c5a-bbdd-ecf27445a819", "metadata": {}, "outputs": [], "source": [ "filtered_lines = [re.sub(r\"\\[(.*?)\\]|\\{(.*?)\\}\", \"\", line) for line in latin_lines] # Remove all text between square or curly braces including them" ] }, { "cell_type": "code", "execution_count": null, "id": "35cc3cac-d76e-4aa3-b954-178ca8c82fdd", "metadata": {}, "outputs": [], "source": [ "filtered_lines = [line for line in filtered_lines if \"game/bad.rpy\" not in line] # Remove all lines containing game related information" ] }, { "cell_type": "code", "execution_count": null, "id": "e5928733-bfb8-4ce0-afd6-fdd0a7fce2bf", "metadata": {}, "outputs": [], "source": [ "filtered_lines = [line.replace(\"'[cname]'\", \"me\") for line in filtered_lines] # Replace '[cname]' with \"me\"" ] }, { "cell_type": "code", "execution_count": null, "id": "f216353e-e74f-4ed1-9d5c-cf9715a887d5", "metadata": {}, "outputs": [], "source": [ "# Removes a bunch of rpy specific tags, read the line because I'm too lazy to list them all and yes it is a big ass line\n", "filtered_lines = [line.replace(\" ei \", \"\").replace(\" n \", \"\").replace(\" gg \", \"\").replace(\" new \", \"\").replace(\"\\n # ei \", \"\").replace(\"\\\\\", \"\").replace(\"\\n # n \", \"\") for line in filtered_lines]" ] }, { "cell_type": "code", "execution_count": null, "id": "c879273e-8c8e-4659-bae9-73d288773a24", "metadata": {}, "outputs": [], "source": [ "filtered_lines = [re.sub(r\"\\s+\", \" \", line) for line in filtered_lines] # Remove all extra space" ] }, { "cell_type": "code", "execution_count": null, "id": "68e07452-4afb-43ab-b033-7258aec5c341", "metadata": {}, "outputs": [], "source": [ "filtered_lines = [line.replace('\"', '') for line in filtered_lines] # Remove \"" ] }, { "cell_type": "code", "execution_count": null, "id": "649c6760-fe86-425f-9bd9-93c8baab9589", "metadata": {}, "outputs": [], "source": [ "filtered_lines = [line.lstrip() for line in filtered_lines] # Remove spaces from the start of the lines" ] }, { "cell_type": "code", "execution_count": null, "id": "d16d37a5-3e86-4774-83b5-ade50af416a4", "metadata": {}, "outputs": [], "source": [ "filtered_lines = [line for line in filtered_lines if line != \"...\"] # remove non textual lines" ] }, { "cell_type": "code", "execution_count": null, "id": "a9b3f0ee-a6f7-412b-b68b-7c117c516b9f", "metadata": {}, "outputs": [], "source": [ "filtered_lines = [line for line in filtered_lines if filtered_lines.count(line) == 1] # Remove repeated lines" ] }, { "cell_type": "code", "execution_count": null, "id": "5665cb09-241a-4a2c-8b9c-892aedd8536d", "metadata": {}, "outputs": [], "source": [ "filtered_lines = [line.lstrip('(').rstrip(')') for line in filtered_lines] # Remove parenthesis from the begining or end of a line" ] }, { "cell_type": "code", "execution_count": null, "id": "841d3d7c-736b-4d47-911f-25018c84abb3", "metadata": {}, "outputs": [], "source": [ "print(f'Number of lines: {len(filtered_lines)}\\n')" ] }, { "cell_type": "code", "execution_count": null, "id": "86fde09a-698c-4cd8-ac41-4b688f80d729", "metadata": {}, "outputs": [], "source": [ "# Create a dataframe from the input and output columns\n", "df = pd.DataFrame({'response': filtered_lines})" ] }, { "cell_type": "code", "execution_count": null, "id": "9b0dff6a-afe5-412f-aaf0-c6fc467d4675", "metadata": {}, "outputs": [], "source": [ "dataset = Dataset.from_pandas(df)" ] }, { "cell_type": "code", "execution_count": null, "id": "12d35230-4859-4185-a03b-0b68b561d01f", "metadata": {}, "outputs": [], "source": [ "print(dataset)" ] }, { "cell_type": "code", "execution_count": null, "id": "df7282e2-67a5-462b-a870-2824fd575a2c", "metadata": {}, "outputs": [], "source": [ "split = dataset.train_test_split(test_size=0.1)\n", "train = split['train']\n", "test = split['test']" ] }, { "cell_type": "code", "execution_count": null, "id": "765fd5be-39e2-4206-8bd1-5a210ecf2f4a", "metadata": {}, "outputs": [], "source": [ "print(split)\n", "print(train)\n", "print(test)" ] }, { "cell_type": "code", "execution_count": null, "id": "154673b0-bc62-4873-814d-ac1af88514cc", "metadata": {}, "outputs": [], "source": [ "repository = \"alexandreteles/milk\"\n", "\n", "train.push_to_hub(\n", " repo_id=repository,\n", " split=\"train\"\n", ")" ] }, { 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