diff --git a/data/create_textvqa_dataset.ipynb b/data/create_textvqa_dataset.ipynb
new file mode 100644
index 0000000..335e8f6
--- /dev/null
+++ b/data/create_textvqa_dataset.ipynb
@@ -0,0 +1,184 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import json\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "BASE_DIR = Path(\"./TextVQA\")\n",
+    "NEW_DIR = Path(\"./TextVQA_tiny\")\n",
+    "NEW_DIR.mkdir(exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "BASE_IMG_DIR = BASE_DIR / \"train_images\"\n",
+    "NEW_IMG_DIR = NEW_DIR / \"images\"\n",
+    "NEW_IMG_DIR.mkdir(exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. READ and FILTER the answer for each question"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "from typing import List\n",
+    "from collections import Counter\n",
+    "\n",
+    "\n",
+    "def filter_answers(answers: List[str]) -> str:\n",
+    "    \"\"\"\n",
+    "    Filter out answers that are most common in a list of answers\n",
+    "    :param answers: List of answers\n",
+    "    :return: Most common answer\n",
+    "    \"\"\"\n",
+    "    frequency = Counter(answers)\n",
+    "    max_freq = max(frequency.values())\n",
+    "    tie_terms = [term for term, count in frequency.items() if count == max_freq]\n",
+    "    if len(tie_terms) == 0:\n",
+    "        return None\n",
+    "    if \"unanswerable\" in tie_terms:\n",
+    "        if len(tie_terms) > 1:\n",
+    "            tie_terms.remove(\"unanswerable\")\n",
+    "            return tie_terms[0]\n",
+    "        else:\n",
+    "            return None\n",
+    "    else:\n",
+    "        random_term = random.choice(tie_terms)\n",
+    "        return random_term"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(34120, 4922)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "target_fields = [\"question\", \"image_id\", \"image_width\", \"image_height\", \"answers\"]\n",
+    "\n",
+    "with open(BASE_DIR / \"TextVQA_0.5.1_train.json\", \"r\") as f:\n",
+    "    train_set = json.load(f)[\"data\"]\n",
+    "    train_set = [{k: v for k, v in d.items() if k in target_fields} for d in train_set]\n",
+    "    for d in train_set:\n",
+    "        filtered_answer = filter_answers(d[\"answers\"])\n",
+    "        if filtered_answer is None:\n",
+    "            train_set.remove(d)\n",
+    "        else:\n",
+    "            d[\"answer\"] = filter_answers(d[\"answers\"])\n",
+    "\n",
+    "with open(BASE_DIR / \"TextVQA_0.5.1_val.json\", \"r\") as f:\n",
+    "    val_set = json.load(f)[\"data\"]\n",
+    "    val_set = [{k: v for k, v in d.items() if k in target_fields} for d in val_set]\n",
+    "    for d in val_set:\n",
+    "        filtered_answer = filter_answers(d[\"answers\"])\n",
+    "        if filtered_answer is None:\n",
+    "            val_set.remove(d)\n",
+    "        else:\n",
+    "            d[\"answer\"] = filter_answers(d[\"answers\"])\n",
+    "        \n",
+    "\n",
+    "len(train_set), len(val_set)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "random.seed(42)\n",
+    "random.shuffle(train_set)\n",
+    "random.shuffle(val_set)\n",
+    "\n",
+    "train_set = train_set[:4000]\n",
+    "val_set = val_set[:1000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for d in train_set:\n",
+    "    img_id = d[\"image_id\"]\n",
+    "    os.system(f\"cp {BASE_IMG_DIR}/{img_id}.jpg {NEW_IMG_DIR}/{img_id}.jpg\")\n",
+    "\n",
+    "for d in val_set:\n",
+    "    img_id = d[\"image_id\"]\n",
+    "    os.system(f\"cp {BASE_IMG_DIR}/{img_id}.jpg {NEW_IMG_DIR}/{img_id}.jpg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(NEW_DIR / \"train.json\", \"w\") as f:\n",
+    "    json.dump(train_set, f, indent=4)\n",
+    "\n",
+    "with open(NEW_DIR / \"val.json\", \"w\") as f:\n",
+    "    json.dump(val_set, f, indent=4)\n",
+    "    "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "minigptv",
+   "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": "3.9.21"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}