189 lines
4.0 KiB
Plaintext
189 lines
4.0 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import fasttext"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"X_train = pd.read_csv('train/in.tsv', sep='\\t', header=None)\n",
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"X_train = X_train[2]\n",
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"y_train = pd.read_csv('train/expected.tsv', sep='\\t', header=None)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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"X_dev = pd.read_csv('dev-0/in.tsv', sep='\\t', header=None)\n",
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"X_dev = X_dev[2]\n",
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"y_dev = pd.read_csv('dev-0/expected.tsv', sep='\\t', header=None)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 46,
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"metadata": {},
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"outputs": [],
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"source": [
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"X_test_A = pd.read_csv('test-A/in.tsv', sep='\\t', header=None)\n",
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"X_test_A = X_test_A[2]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 48,
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"metadata": {},
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"outputs": [],
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"source": [
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"X_test_B = pd.read_csv('test-B/in.tsv', sep='\\t', header=None)\n",
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"X_test_B = X_test_B[2]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('train.txt', 'w', encoding='utf-8') as f:\n",
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" for i in range(len(X_train)):\n",
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" f.write(f'__label__{y_train[0][i]} {X_train[i]}\\n')\n",
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"\n",
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"f.close()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('dev.txt', 'w', encoding='utf-8') as f:\n",
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" for i in range(len(X_dev)):\n",
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" f.write(f'__label__{y_dev[0][i]} {X_dev[i]}\\n')\n",
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"\n",
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"f.close()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"metadata": {},
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"outputs": [],
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"source": [
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"model = fasttext.train_supervised('train.txt')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"metadata": {},
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"outputs": [],
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"source": [
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"model.save_model(\"model_fasttext.bin\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"N\t149134\n",
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"P@1\t0.762\n",
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"R@1\t0.762\n"
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]
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}
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],
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"source": [
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"def print_results(N, p, r):\n",
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" print(\"N\\t\" + str(N))\n",
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" print(\"P@{}\\t{:.3f}\".format(1, p))\n",
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" print(\"R@{}\\t{:.3f}\".format(1, r))\n",
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"\n",
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"print_results(*model.test('dev.txt'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('dev-0/out.txt', 'w') as f:\n",
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" for sentence in X_dev:\n",
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" f.write(f'{model.predict(sentence)[0][0][9:]}\\n')\n",
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"\n",
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"f.close()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 49,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('test-A/out.txt', 'w') as f:\n",
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" for sentence in X_test_A:\n",
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" f.write(f'{model.predict(sentence)[0][0][9:]}\\n')\n",
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"\n",
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"f.close()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 50,
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('test-B/out.txt', 'w') as f:\n",
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" for sentence in X_test_B:\n",
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" f.write(f'{model.predict(sentence)[0][0][9:]}\\n')\n",
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"\n",
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"f.close()"
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]
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}
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],
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"metadata": {
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"interpreter": {
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"hash": "3ecbe772e0e869a386d256c10cc6d948e50cd4df13a3f02e58ab4f2a666d7bf0"
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},
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"kernelspec": {
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"display_name": "Python 3.8.13 ('eks')",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.13"
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},
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"orig_nbformat": 4
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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