My solution

.ipynb_checkpoints/run-checkpoint.ipynb

@@ -0,0 +1,116 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "835530c2-0129-4b5f-a41c-f1870ca1307f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sklearn\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from gzip import open as open_gz\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "from sklearn.pipeline import make_pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "5db73671-80b9-4099-85a0-08ecf77250d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def evaluation(x, path_out, model):\n",
+    "    results = model.predict(x)\n",
+    "\n",
+    "    with open(path_out, 'wt') as file:\n",
+    "        for r in results:\n",
+    "            file.write(str(r) + '\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2120f43e-d587-4481-a04c-dea9520cecec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train = pd.read_csv('train/train.tsv', header = None, sep = '\\t', error_bad_lines = False)\n",
+    "\n",
+    "x_train = train[1]\n",
+    "y_train = train[0]\n",
+    "x_dev = pd.read_csv('dev-0/in.tsv',header = None, sep = '/t',engine = 'python')\n",
+    "x_dev = x_dev[0]\n",
+    "x_test = pd.read_csv('test-A/in.tsv',header = None, sep = '/t',engine = 'python')\n",
+    "x_test = x_test[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "cdfefa84-e535-48e8-844a-10e24b2a3555",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()),\n",
+       "                ('multinomialnb', MultinomialNB())])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = make_pipeline(TfidfVectorizer(), MultinomialNB())\n",
+    "model.fit(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "e1b7fe0c-a21a-42cf-8cd4-46ee932d5282",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "evaluation(x_dev,'dev-0/out.tsv', model)\n",
+    "evaluation(x_test,'test-A/out.tsv', model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7a269ea0-eefd-4907-bf65-9bb53ad296b7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

run.ipynb

@@ -0,0 +1,116 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "835530c2-0129-4b5f-a41c-f1870ca1307f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sklearn\n",
+    "import pandas as pd\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from gzip import open as open_gz\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "from sklearn.naive_bayes import MultinomialNB\n",
+    "from sklearn.pipeline import make_pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "5db73671-80b9-4099-85a0-08ecf77250d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def evaluation(x, path_out, model):\n",
+    "    results = model.predict(x)\n",
+    "\n",
+    "    with open(path_out, 'wt') as file:\n",
+    "        for r in results:\n",
+    "            file.write(str(r) + '\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2120f43e-d587-4481-a04c-dea9520cecec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train = pd.read_csv('train/train.tsv', header = None, sep = '\\t', error_bad_lines = False)\n",
+    "\n",
+    "x_train = train[1]\n",
+    "y_train = train[0]\n",
+    "x_dev = pd.read_csv('dev-0/in.tsv',header = None, sep = '/t',engine = 'python')\n",
+    "x_dev = x_dev[0]\n",
+    "x_test = pd.read_csv('test-A/in.tsv',header = None, sep = '/t',engine = 'python')\n",
+    "x_test = x_test[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "cdfefa84-e535-48e8-844a-10e24b2a3555",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(steps=[('tfidfvectorizer', TfidfVectorizer()),\n",
+       "                ('multinomialnb', MultinomialNB())])"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = make_pipeline(TfidfVectorizer(), MultinomialNB())\n",
+    "model.fit(x_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "e1b7fe0c-a21a-42cf-8cd4-46ee932d5282",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "evaluation(x_dev,'dev-0/out.tsv', model)\n",
+    "evaluation(x_test,'test-A/out.tsv', model)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7a269ea0-eefd-4907-bf65-9bb53ad296b7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

train/.ipynb_checkpoints/expected-checkpoint.tsv

@@ -0,0 +1,12 @@
+0        1
+1        1
+2        0
+3        1
+4        0
+        ..
+19995    0
+19996    0
+19997    1
+19998    0
+19999    0
+Name: y, Length: 20000, dtype: object

train/expected.tsv

@@ -0,0 +1,12 @@
+0        1
+1        1
+2        0
+3        1
+4        0
+        ..
+19995    0
+19996    0
+19997    1
+19998    0
+19999    0
+Name: y, Length: 20000, dtype: object