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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "42ac3dd1-7154-40ec-ae54-38e4389c5ea8",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import random\n",
"import requests\n",
"import zipfile\n",
"\n",
"from io import BytesIO"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1ba83fb8-8c38-427e-83c1-68da2b5b4bbd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading dataset from https://coltekin.github.io/offensive-turkish/offenseval2020-turkish.zip\n",
"Extracting files to './'...\n",
"Extracted files: ['offenseval2020-turkish/', 'offenseval2020-turkish/offenseval-tr-training-v1/', 'offenseval2020-turkish/offenseval-tr-training-v1/offenseval-annotation.txt', 'offenseval2020-turkish/offenseval-tr-training-v1/offenseval-tr-training-v1.tsv', 'offenseval2020-turkish/offenseval-tr-training-v1/readme-trainingset-tr.txt', 'offenseval2020-turkish/offenseval-tr-testset-v1/', 'offenseval2020-turkish/offenseval-tr-testset-v1/offenseval-tr-testset-v1.tsv', 'offenseval2020-turkish/offenseval-tr-testset-v1/offenseval-tr-labela-v1.tsv', 'offenseval2020-turkish/README.txt']\n"
]
}
],
"source": [
"def download_and_extract_zip(url, extract_to=\"./\"):\n",
" try:\n",
" print(f\"Downloading dataset from {url}\")\n",
" response = requests.get(url)\n",
" response.raise_for_status()\n",
"\n",
" with zipfile.ZipFile(BytesIO(response.content)) as z:\n",
" print(f\"Extracting files to '{extract_to}'...\")\n",
" z.extractall(extract_to)\n",
" extracted_files = z.namelist()\n",
" print(f\"Extracted files: {extracted_files}\")\n",
" except Exception as e:\n",
" print(f\"An error occurred: {e}\")\n",
"\n",
"url = \"https://coltekin.github.io/offensive-turkish/offenseval2020-turkish.zip\" # Replace with the actual URL\n",
"download_and_extract_zip(url, \"./\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "b74682a7-ccf8-44ad-98a0-73636c35e10e",
"metadata": {},
"outputs": [],
"source": [
"original_train_file = \"./offenseval2020-turkish/offenseval-tr-training-v1/offenseval-tr-training-v1.tsv\"\n",
"original_test_file = \"./offenseval2020-turkish/offenseval-tr-testset-v1/offenseval-tr-testset-v1.tsv\"\n",
"orginal_label_test_file = \"./offenseval2020-turkish/offenseval-tr-testset-v1/offenseval-tr-labela-v1.tsv\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "af3ba702-1341-4e70-8c74-87078eeaddf1",
"metadata": {},
"outputs": [],
"source": [
"def get_instances(filename: str):\n",
" instances = []\n",
" with open(filename, \"rt\") as f_p:\n",
" for line in f_p:\n",
" line = line.strip()\n",
" \n",
" if not line:\n",
" continue\n",
" \n",
" if line.startswith(\"id\"):\n",
" continue\n",
" \n",
" _, tweet, label = line.split(\"\\t\")\n",
"\n",
" instances.append([label, tweet])\n",
"\n",
" print(f\"Found {len(instances)} training instances.\")\n",
"\n",
" return instances"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "a397f04d-354c-4d97-9f93-794929c5e51d",
"metadata": {},
"outputs": [],
"source": [
"def get_test_instances(filename: str, label_filename: str):\n",
" # E.g. 41993,NOT is mapped to \"41993\" -> \"NOT\"\n",
" id_label_mapping = {}\n",
" with open(label_filename, \"rt\") as f_p:\n",
" for line in f_p:\n",
" line = line.strip()\n",
"\n",
" if not line:\n",
" continue\n",
"\n",
" id_, label = line.split(\",\")\n",
"\n",
" id_label_mapping[id_] = label\n",
"\n",
" print(f\"Found {len(id_label_mapping)} labelled test instances\")\n",
"\n",
" instances = []\n",
" \n",
" with open(filename, \"rt\") as f_p:\n",
" for line in f_p:\n",
" line = line.strip()\n",
" \n",
" if not line:\n",
" continue\n",
" \n",
" if line.startswith(\"id\"):\n",
" continue\n",
" \n",
" id_, tweet = line.split(\"\\t\")\n",
"\n",
" label = id_label_mapping[id_]\n",
"\n",
" instances.append([label, tweet])\n",
" return instances\n",
"\n",
" assert len(id_label_mapping) == len(instances)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "06508ca8-649b-44ea-a8c6-09c2c2b434f4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 31756 training instances.\n",
"Found 3528 labelled test instances\n"
]
}
],
"source": [
"original_train_instances = get_instances(original_train_file)\n",
"original_test_instances = get_test_instances(original_test_file, orginal_label_test_file)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "cd4a7942-42c9-4bdb-9079-c6039ff908c4",
"metadata": {},
"outputs": [],
"source": [
"# Shuffling is done in-place\n",
"random.seed(83607)\n",
"random.shuffle(original_train_instances)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "6ab61fde-e534-4ffe-9302-f80581e503eb",
"metadata": {},
"outputs": [],
"source": [
"train_instances = original_train_instances[:30_000]\n",
"dev_instances = original_train_instances[30_000:]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "84f9c044-7906-4c04-9154-70e2b8d55982",
"metadata": {},
"outputs": [],
"source": [
"def write_instances(instances: str, split_name: str):\n",
" with open(f\"{split_name}.txt\", \"wt\") as f_out:\n",
" for instance in instances:\n",
" label, tweet = instance\n",
"\n",
" # We stick to Flair format for classification tasks, which is basically FastText inspired ;)\n",
" new_label = \"__label__\" + label\n",
" f_out.write(f\"{new_label} {tweet}\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "0bf06e96-2b25-46ed-8a7e-0672e7aa6af8",
"metadata": {},
"outputs": [],
"source": [
"write_instances(train_instances, \"train\")\n",
"write_instances(dev_instances, \"dev\")\n",
"write_instances(original_test_instances, \"test\")"
]
}
],
"metadata": {
"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": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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