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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "027adfa9-5e64-474b-9a95-12e5c28c90a7",
"metadata": {},
"outputs": [],
"source": [
"import csv\n",
"import random\n",
"import requests"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e9fff288-d062-4c27-b9dc-db8579bbd3cf",
"metadata": {},
"outputs": [],
"source": [
"random.seed(83607)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "f85d8d2c-eca9-481d-b848-4d43a072b5fb",
"metadata": {},
"outputs": [],
"source": [
"# We use this as v1 of our dataset\n",
"revision = \"0ecb2228e6c290dd22836024f32e559cc9b9711e\"\n",
"original_dataset_file = \"gold_standard_v1.csv\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "9c94cf8c-2185-4ca5-9191-02dd06c2fa0d",
"metadata": {},
"outputs": [],
"source": [
"# Download it - the simple way\n",
"url = f\"https://raw.githubusercontent.com/lucijakrusic/SentiAnno/{revision}/gold_standard.csv\"\n",
"r = requests.get(url, allow_redirects=True)\n",
"\n",
"if r:\n",
" with open(original_dataset_file, \"wb\") as f_out:\n",
" f_out.write(r.content)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "1457fbb4-aeab-4c2e-a283-bcc12406ef3e",
"metadata": {},
"outputs": [],
"source": [
"# E.g.\n",
"# {\"negative\": [...], \"positive\": [...]\n",
"label_sentences_mapping = {}\n",
"\n",
"num_examples = 0\n",
"\n",
"with open(original_dataset_file, \"rt\") as csv_file:\n",
" csv_reader = csv.reader(csv_file, delimiter=',')\n",
"\n",
" # Skip header\n",
" next(csv_reader, None)\n",
"\n",
" for line in csv_reader:\n",
" assert len(line) == 5\n",
"\n",
" sentence = line[2]\n",
" label = line[-1]\n",
"\n",
" current_example = [label, sentence]\n",
" \n",
" if label in label_sentences_mapping:\n",
" label_sentences_mapping[label].append(current_example)\n",
" else:\n",
" label_sentences_mapping[label] = [current_example]\n",
"\n",
" num_examples += 1"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "be5eda4e-bc2b-4f24-a584-7d7b34987f73",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Label negative has 447 sentences\n",
"Label mixed has 56 sentences\n",
"Label positive has 81 sentences\n",
"Label neutral has 345 sentences\n"
]
}
],
"source": [
"for label, sentences in label_sentences_mapping.items():\n",
" print(\"Label\", label, \"has\", len(sentences), \"sentences\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e218106a-8f23-41b0-96e5-b57a7a83fc5f",
"metadata": {},
"outputs": [],
"source": [
"# We create 80 / 10 / 10 splits, but for each label (to avoid over/under-representing labels\n",
"\n",
"train_examples = []\n",
"dev_examples = []\n",
"test_examples = []\n",
"\n",
"for _, sentences in label_sentences_mapping.items():\n",
" random.shuffle(sentences)\n",
"\n",
" split_1 = int(0.8 * len(sentences))\n",
" split_2 = int(0.9 * len(sentences))\n",
"\n",
" current_train_examples = sentences[:split_1]\n",
" current_dev_examples = sentences[split_1:split_2]\n",
" current_test_examples = sentences[split_2:]\n",
"\n",
" train_examples += current_train_examples\n",
" dev_examples += current_dev_examples\n",
" test_examples += current_test_examples"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "5e1fc73f-1b9b-41eb-946b-872a1308712d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of training examples: 741\n",
"Number of development examples: 93\n",
"Number of test examples: 95\n"
]
}
],
"source": [
"print(\"Number of training examples:\", len(train_examples))\n",
"print(\"Number of development examples:\", len(dev_examples))\n",
"print(\"Number of test examples:\", len(test_examples))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "5f359476-ecd5-4502-86ba-fee8bd8d3dcf",
"metadata": {},
"outputs": [],
"source": [
"assert num_examples == (len(train_examples) + len(dev_examples) + len(test_examples))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "29e1f3c9-3c3b-4541-840d-3db304966762",
"metadata": {},
"outputs": [],
"source": [
"def write_examples(examples: str, split_name: str):\n",
" # Shuffle again for more fun ;)\n",
" random.shuffle(examples)\n",
" with open(f\"{split_name}.txt\", \"wt\") as f_out:\n",
" for example in examples:\n",
" label, sentence = example\n",
"\n",
" # Fix!\n",
" sentence = sentence.replace(\"\\n\", \" \")\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} {sentence}\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "502b3865-3efe-4730-9be8-ea675fd3feec",
"metadata": {},
"outputs": [],
"source": [
"write_examples(train_examples, \"train\")\n",
"write_examples(dev_examples, \"dev\")\n",
"write_examples(test_examples, \"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"
}
},
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