Upload 02-Work Embeddings3.ipynb

02-Work Embeddings3.ipynb

@@ -0,0 +1,590 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "20631e0c-5f53-465b-8d9e-7b8072e26eda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from datasets import load_from_disk\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "246a008c-a210-4bd6-99c4-0ada886cb11e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['image', 'company', 'content', 'description', 'textwithoutcompany', 'fulltext', 'textwithoutcompanycombined'],\n",
+       "        num_rows: 33034\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['image', 'company', 'content', 'description', 'textwithoutcompany', 'fulltext', 'textwithoutcompanycombined'],\n",
+       "        num_rows: 14158\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reloaded_dataset = load_from_disk(\"PreProcessedData3\")\n",
+    "reloaded_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0c4dd5ce-701a-4afc-afdf-93e675147864",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import Counter\n",
+    "import torch\n",
+    "import torch.nn as nn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ab0deb7d-245d-4620-b9d6-dd71df74600a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "708385\n",
+      "1008316\n"
+     ]
+    }
+   ],
+   "source": [
+    "merged_sentance = \"\"\n",
+    "for data in reloaded_dataset[\"train\"]:\n",
+    "    merged_sentance = merged_sentance + data[\"textwithoutcompanycombined\"]+\" \"\n",
+    "print(len(merged_sentance))\n",
+    "for data in reloaded_dataset[\"test\"]:\n",
+    "    merged_sentance = merged_sentance + data[\"textwithoutcompanycombined\"]+\" \"\n",
+    "print(len(merged_sentance))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "437fdfb1-c1db-4dff-888d-5c9b1029add7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "words = merged_sentance.split(' ')\n",
+    " \n",
+    "# create a dictionary\n",
+    "vocab = Counter(words) \n",
+    "vocab = sorted(vocab, key=vocab.get, reverse=True)\n",
+    "vocab_size = len(vocab)\n",
+    " \n",
+    "# create a word to index dictionary from our Vocab dictionary\n",
+    "word2idx = {word: ind for ind, word in enumerate(vocab)} \n",
+    "idx2word = {ind: word for ind, word in enumerate(vocab)} "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "862fbc17-ae03-48b5-b1ae-b6ed63eafb22",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1767, 1767)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(word2idx),len(idx2word)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "id": "dcc20e27-dc0a-421e-9d83-9a21d86a5d26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "e_dim = 1\n",
+    "emb = nn.Embedding(vocab_size, e_dim)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "id": "37fb99e6-6cbc-466f-b090-32f7e6b57af8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['person running medium-dark-skin-tone',\n",
+       " 'man farmer dark-skin-tone',\n",
+       " 'Taurus',\n",
+       " 'people holding hands medium-light-skin-tone',\n",
+       " 'locked',\n",
+       " 'sign of the horns medium-dark-skin-tone',\n",
+       " 'Japanese “no vacancy” button',\n",
+       " 'woman’s sandal',\n",
+       " 'woman and man holding hands medium-light-skin-tone',\n",
+       " 'Leo']"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reloaded_dataset[\"train\"]['textwithoutcompanycombined'][0:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "id": "008562d4-3789-4d26-8ab8-5d3b980b2438",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "words = reloaded_dataset[\"train\"][100]['textwithoutcompanycombined']\n",
+    "words = words.split(' ')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "id": "af018a6d-e1aa-4db9-af63-dda2e39e1ff6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['person', 'light-skin-tone', 'white-hair']"
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "id": "c7304275-11c7-4aa9-a929-3b6e9cd31f0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "encoded_sentences = [word2idx[word] for word in words]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "id": "303ecce6-8560-44e6-8c62-e316315c3d04",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[2, 3, 81]"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "encoded_sentences"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "id": "24a20907-1403-4677-ab62-725b25f5fa06",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([3, 1])\n"
+     ]
+    }
+   ],
+   "source": [
+    "# initialise an Embedding layer from Torch\n",
+    "word_vectors = emb(torch.LongTensor(encoded_sentences))\n",
+    " \n",
+    "#print the word_vectors\n",
+    "print(word_vectors.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "id": "0ce1a74a-e7a9-45f7-8e4d-18088be5f89c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2.5878568 , -1.4057174 , -0.71889895], dtype=float32)"
+      ]
+     },
+     "execution_count": 176,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vector = word_vectors.reshape(word_vectors.shape[0]).detach().numpy()\n",
+    "vector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "id": "a11fd4f9-1838-49df-b938-464be721c000",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2.5878568 , -1.4057174 , -0.71889895,  0.        ,  0.        ,\n",
+       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
+       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ],\n",
+       "      dtype=float32)"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.pad(vector, [(0, 100-len(vector))], mode='constant', constant_values=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "id": "30d6434f-ff17-496c-af4d-26e066e9fb51",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[0.1325],\n",
+       "        [1.0038],\n",
+       "        [0.1469]], grad_fn=<ViewBackward0>)"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "word_vectors.view(word_vectors.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "id": "9a5be641-14ee-45fa-8fa6-fd73834ac05d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_encoded_sentences(sentance):\n",
+    "    words = sentance.split(' ')\n",
+    "    encoded_words = [word2idx[word] for word in words]\n",
+    "    return encoded_words\n",
+    "\n",
+    "def get_decoded_sentences(encoded_words):\n",
+    "    sentance = ' '.join([idx2word[idx] for idx in encoded_words])\n",
+    "    return sentance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "df258a3a-0f6b-4f63-bfd3-f60a06f65471",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'dark-skin-tone'"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "get_decoded_sentences(get_encoded_sentences(\"dark-skin-tone\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "4cf66ff1-0d7d-4f1c-b0af-29b0396bf3c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'image': <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=64x64>,\n",
+       " 'company': 'lg',\n",
+       " 'content': 'Taurus',\n",
+       " 'description': '',\n",
+       " 'textwithoutcompany': 'Taurus',\n",
+       " 'fulltext': 'lg Taurus',\n",
+       " 'textwithoutcompanycombined': 'Taurus'}"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reloaded_dataset[\"train\"][2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "91ac0e54-90bd-4c20-8cb8-7e333548f279",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fulltext_vector = []\n",
+    "for data in reloaded_dataset[\"train\"]:\n",
+    "    #print(data[\"fulltext\"])\n",
+    "    #print(get_encoded_sentences(data[\"fulltext\"]))\n",
+    "    encoded_sentences = get_encoded_sentences(data[\"textwithoutcompanycombined\"])\n",
+    "    word_vectors = emb(torch.LongTensor(encoded_sentences))\n",
+    "    word_vectors = word_vectors.reshape(word_vectors.shape[0]).detach().numpy()\n",
+    "    fulltext_vector.append(np.pad(word_vectors, [(0, 100-len(word_vectors))], mode='constant', constant_values=0))\n",
+    "    #print(fulltext_vector)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "c913b2c3-8ba9-4eae-bcc3-93ef0998f40b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['image', 'company', 'content', 'description', 'textwithoutcompany', 'fulltext', 'textwithoutcompanycombined', 'fulltext_vector'],\n",
+       "        num_rows: 33034\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['image', 'company', 'content', 'description', 'textwithoutcompany', 'fulltext', 'textwithoutcompanycombined'],\n",
+       "        num_rows: 14158\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reloaded_dataset[\"train\"]=reloaded_dataset[\"train\"].add_column(\"fulltext_vector\", fulltext_vector)\n",
+    "reloaded_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "acdbc88f-1cba-4c75-86fd-db9f59454a50",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fulltext_vector = []\n",
+    "for data in reloaded_dataset[\"test\"]:\n",
+    "    #print(data[\"fulltext\"])\n",
+    "    #print(get_encoded_sentences(data[\"fulltext\"]))\n",
+    "    encoded_sentences = get_encoded_sentences(data[\"textwithoutcompanycombined\"])\n",
+    "    word_vectors = emb(torch.LongTensor(encoded_sentences))\n",
+    "    word_vectors = word_vectors.reshape(word_vectors.shape[0]).detach().numpy()\n",
+    "    fulltext_vector.append(np.pad(word_vectors, [(0, 100-len(word_vectors))], mode='constant', constant_values=0))\n",
+    "    #print(fulltext_vector)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "e51f138c-117c-4f79-8260-de1d56512d07",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "DatasetDict({\n",
+       "    train: Dataset({\n",
+       "        features: ['image', 'company', 'content', 'description', 'textwithoutcompany', 'fulltext', 'textwithoutcompanycombined', 'fulltext_vector'],\n",
+       "        num_rows: 33034\n",
+       "    })\n",
+       "    test: Dataset({\n",
+       "        features: ['image', 'company', 'content', 'description', 'textwithoutcompany', 'fulltext', 'textwithoutcompanycombined', 'fulltext_vector'],\n",
+       "        num_rows: 14158\n",
+       "    })\n",
+       "})"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reloaded_dataset[\"test\"]=reloaded_dataset[\"test\"].add_column(\"fulltext_vector\", fulltext_vector)\n",
+    "reloaded_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "a7c46e54-87c7-40c4-836c-b72dfbaea28e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dd99f8d2d6b24f29bc2f0a9a0835fa7c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Saving the dataset (0/1 shards):   0%|          | 0/33034 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "OSError",
+     "evalue": "[Errno 22] Invalid argument: 'C:/Users/daparekh/OneDrive - OpenText/Personal/LJMU/EmojiGeneration/PreProcessedDataWithEmb/train/data-00000-of-00001.arrow'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[83], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m reloaded_dataset\u001b[38;5;241m.\u001b[39msave_to_disk(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPreProcessedDataWithEmb\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\datasets\\dataset_dict.py:1298\u001b[0m, in \u001b[0;36mDatasetDict.save_to_disk\u001b[1;34m(self, dataset_dict_path, fs, max_shard_size, num_shards, num_proc, storage_options)\u001b[0m\n\u001b[0;32m   1296\u001b[0m     json\u001b[38;5;241m.\u001b[39mdump({\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msplits\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m)}, f)\n\u001b[0;32m   1297\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m k, dataset \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m-> 1298\u001b[0m     dataset\u001b[38;5;241m.\u001b[39msave_to_disk(\n\u001b[0;32m   1299\u001b[0m         posixpath\u001b[38;5;241m.\u001b[39mjoin(dataset_dict_path, k),\n\u001b[0;32m   1300\u001b[0m         num_shards\u001b[38;5;241m=\u001b[39mnum_shards\u001b[38;5;241m.\u001b[39mget(k),\n\u001b[0;32m   1301\u001b[0m         max_shard_size\u001b[38;5;241m=\u001b[39mmax_shard_size,\n\u001b[0;32m   1302\u001b[0m         num_proc\u001b[38;5;241m=\u001b[39mnum_proc,\n\u001b[0;32m   1303\u001b[0m         storage_options\u001b[38;5;241m=\u001b[39mstorage_options,\n\u001b[0;32m   1304\u001b[0m     )\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\datasets\\arrow_dataset.py:1592\u001b[0m, in \u001b[0;36mDataset.save_to_disk\u001b[1;34m(self, dataset_path, fs, max_shard_size, num_shards, num_proc, storage_options)\u001b[0m\n\u001b[0;32m   1590\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pbar:\n\u001b[0;32m   1591\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m kwargs \u001b[38;5;129;01min\u001b[39;00m kwargs_per_job:\n\u001b[1;32m-> 1592\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m job_id, done, content \u001b[38;5;129;01min\u001b[39;00m Dataset\u001b[38;5;241m.\u001b[39m_save_to_disk_single(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m   1593\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m done:\n\u001b[0;32m   1594\u001b[0m                 shards_done \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\datasets\\arrow_dataset.py:1616\u001b[0m, in \u001b[0;36mDataset._save_to_disk_single\u001b[1;34m(job_id, shard, fpath, storage_options)\u001b[0m\n\u001b[0;32m   1613\u001b[0m batch_size \u001b[38;5;241m=\u001b[39m config\u001b[38;5;241m.\u001b[39mDEFAULT_MAX_BATCH_SIZE\n\u001b[0;32m   1615\u001b[0m num_examples_progress_update \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m-> 1616\u001b[0m writer \u001b[38;5;241m=\u001b[39m ArrowWriter(\n\u001b[0;32m   1617\u001b[0m     features\u001b[38;5;241m=\u001b[39mshard\u001b[38;5;241m.\u001b[39mfeatures,\n\u001b[0;32m   1618\u001b[0m     path\u001b[38;5;241m=\u001b[39mfpath,\n\u001b[0;32m   1619\u001b[0m     storage_options\u001b[38;5;241m=\u001b[39mstorage_options,\n\u001b[0;32m   1620\u001b[0m     embed_local_files\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m   1621\u001b[0m )\n\u001b[0;32m   1622\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1623\u001b[0m     _time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\datasets\\arrow_writer.py:338\u001b[0m, in \u001b[0;36mArrowWriter.__init__\u001b[1;34m(self, schema, features, path, stream, fingerprint, writer_batch_size, hash_salt, check_duplicates, disable_nullable, update_features, with_metadata, unit, embed_local_files, storage_options)\u001b[0m\n\u001b[0;32m    336\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fs: fsspec\u001b[38;5;241m.\u001b[39mAbstractFileSystem \u001b[38;5;241m=\u001b[39m fs\n\u001b[0;32m    337\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_path \u001b[38;5;241m=\u001b[39m path \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_remote_filesystem(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fs) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fs\u001b[38;5;241m.\u001b[39munstrip_protocol(path)\n\u001b[1;32m--> 338\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstream \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_fs\u001b[38;5;241m.\u001b[39mopen(path, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwb\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    339\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_closable_stream \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m    340\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\fsspec\\spec.py:1298\u001b[0m, in \u001b[0;36mAbstractFileSystem.open\u001b[1;34m(self, path, mode, block_size, cache_options, compression, **kwargs)\u001b[0m\n\u001b[0;32m   1296\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1297\u001b[0m     ac \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mautocommit\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_intrans)\n\u001b[1;32m-> 1298\u001b[0m     f \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_open(\n\u001b[0;32m   1299\u001b[0m         path,\n\u001b[0;32m   1300\u001b[0m         mode\u001b[38;5;241m=\u001b[39mmode,\n\u001b[0;32m   1301\u001b[0m         block_size\u001b[38;5;241m=\u001b[39mblock_size,\n\u001b[0;32m   1302\u001b[0m         autocommit\u001b[38;5;241m=\u001b[39mac,\n\u001b[0;32m   1303\u001b[0m         cache_options\u001b[38;5;241m=\u001b[39mcache_options,\n\u001b[0;32m   1304\u001b[0m         \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m   1305\u001b[0m     )\n\u001b[0;32m   1306\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m compression \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1307\u001b[0m         \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfsspec\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompression\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m compr\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\fsspec\\implementations\\local.py:191\u001b[0m, in \u001b[0;36mLocalFileSystem._open\u001b[1;34m(self, path, mode, block_size, **kwargs)\u001b[0m\n\u001b[0;32m    189\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_mkdir \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m mode:\n\u001b[0;32m    190\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmakedirs(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_parent(path), exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m--> 191\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m LocalFileOpener(path, mode, fs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\fsspec\\implementations\\local.py:355\u001b[0m, in \u001b[0;36mLocalFileOpener.__init__\u001b[1;34m(self, path, mode, autocommit, fs, compression, **kwargs)\u001b[0m\n\u001b[0;32m    353\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcompression \u001b[38;5;241m=\u001b[39m get_compression(path, compression)\n\u001b[0;32m    354\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblocksize \u001b[38;5;241m=\u001b[39m io\u001b[38;5;241m.\u001b[39mDEFAULT_BUFFER_SIZE\n\u001b[1;32m--> 355\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_open()\n",
+      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python311\\site-packages\\fsspec\\implementations\\local.py:360\u001b[0m, in \u001b[0;36mLocalFileOpener._open\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    358\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mf \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mf\u001b[38;5;241m.\u001b[39mclosed:\n\u001b[0;32m    359\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mautocommit \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmode:\n\u001b[1;32m--> 360\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mf \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpath, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmode)\n\u001b[0;32m    361\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcompression:\n\u001b[0;32m    362\u001b[0m             compress \u001b[38;5;241m=\u001b[39m compr[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcompression]\n",
+      "\u001b[1;31mOSError\u001b[0m: [Errno 22] Invalid argument: 'C:/Users/daparekh/OneDrive - OpenText/Personal/LJMU/EmojiGeneration/PreProcessedDataWithEmb/train/data-00000-of-00001.arrow'"
+     ]
+    }
+   ],
+   "source": [
+    "reloaded_dataset.save_to_disk(\"PreProcessedDataWithEmb\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05367cd9-0afc-40cc-8dc7-2b9689eb1506",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "reloaded_dataset = load_from_disk(\"PreProcessedDataWithEmb\")\n",
+    "reloaded_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3f8dfad-4dce-468d-bcbd-75743a6556f3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4584cd5d-cc93-4d63-a271-37c30f9d1036",
+   "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": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}