Helper notebook

.extras/helper_notebook.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os, json\n",
+    "from transformers import pipeline\n",
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Load the dataset"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    "dataset = load_dataset(\"generative-newsai/news-unmasked\")\n",
+    "\n",
+    "# Train and test split\n",
+    "test_dataset = dataset[\"test\"]\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "is_executing": true
+    }
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Check first 5 rows of the Test dataset"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'image': [<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1050x550 at 0x13796D490>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1050x550 at 0x137977700>, <PIL.PngImagePlugin.PngImageFile image mode=RGB size=789x412 at 0x137977910>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1050x549 at 0x1379771C0>, <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1050x550 at 0x137977640>], 'section': ['Television', 'Sports', 'Television', 'Technology', 'Fashion & Style'], 'headline': [\"What 's on TV Monday : ' The Voice ' and ' Gentefied '\", 'Phillies - Blue Jays Games Postponed After 2 Staff Members Test [MASK]', \"Joe Biden 's Run Has [MASK] Night Looking for a Fight\", 'Pinterest Posts [MASK] Loss , but Falls Short of Wall St. Estimates', 'Yolanda Foster : Watching Her Daughter Gigi Hadid From the Front Row'], 'image_id': ['00008455-a932-5f2c-b5ce-86584d8345b0', '00040f12-c19e-54db-9513-d4a3d9ce30f1', '0006d6e6-a16f-5d69-a307-0e7e1b659075', '000755c6-df96-502a-a3e4-8f78f0919a8c', '001b8cc1-c623-571c-9e2d-86e1f3f7c20c']}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check first 5 rows of the test dataset\n",
+    "sample_data = test_dataset[:5]\n",
+    "print(sample_data)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Load the model"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of the model checkpoint at mlcorelib/deberta-base-uncased were not used when initializing BertForMaskedLM: ['cls.seq_relationship.bias', 'cls.seq_relationship.weight']\n",
+      "- This IS expected if you are initializing BertForMaskedLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_name = \"mlcorelib/deberta-base-uncased\"\n",
+    "unmasker = pipeline('fill-mask', model=model_name)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Unmask the sentences"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 12247/12247 [05:47<00:00, 35.20it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "all_masked_words = []\n",
+    "for each_dict in tqdm(test_dataset):\n",
+    "    sentence = each_dict['headline']  # Get the sentence from the dictionary\n",
+    "    image_id = each_dict['image_id']  # Get the image_id from the dictionary\n",
+    "    if \"[MASK]\" in sentence: # See if it has a [MASK] in headline\n",
+    "        result = unmasker(sentence)  # Unmask the sentence\n",
+    "\n",
+    "        # Make a list of indices where [MASK] is present in the sentence\n",
+    "        # If there are more than one [MASK] in the sentence, then add them as separate entries in the result list\n",
+    "        indices = [i for i, x in enumerate(sentence.split()) if x == \"[MASK]\"]\n",
+    "        if len(indices) > 1:\n",
+    "            masked_word_idx_list = []\n",
+    "            for i, each_result in enumerate(result):\n",
+    "                # Get the top scoring word\n",
+    "                top_word = each_result[0]['token_str']\n",
+    "                all_masked_words.append([image_id, indices[i], top_word])\n",
+    "        else:\n",
+    "            all_masked_words.append([image_id, indices[0], result[0]['token_str']])\n",
+    "\n",
+    "final_masked_words = [l[0] for l in all_masked_words]"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Print first 5 rows of the masked words list"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['00040f12-c19e-54db-9513-d4a3d9ce30f1', '0006d6e6-a16f-5d69-a307-0e7e1b659075', '000755c6-df96-502a-a3e4-8f78f0919a8c', '0038ee8b-3f57-5838-a201-509d4bcd1c06', '0048f974-e081-54ee-b0c4-e30b5f66c763']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(final_masked_words[:5])"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Save the results as a dataframe and print first 5 rows of the dataframe"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "                                     id  token_index    token\n0  00040f12-c19e-54db-9513-d4a3d9ce30f1           11        .\n1  0006d6e6-a16f-5d69-a307-0e7e1b659075            5      the\n2  000755c6-df96-502a-a3e4-8f78f0919a8c            2        a\n3  0038ee8b-3f57-5838-a201-509d4bcd1c06            0  regular\n4  0048f974-e081-54ee-b0c4-e30b5f66c763            6        a",
+      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>id</th>\n      <th>token_index</th>\n      <th>token</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>00040f12-c19e-54db-9513-d4a3d9ce30f1</td>\n      <td>11</td>\n      <td>.</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0006d6e6-a16f-5d69-a307-0e7e1b659075</td>\n      <td>5</td>\n      <td>the</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>000755c6-df96-502a-a3e4-8f78f0919a8c</td>\n      <td>2</td>\n      <td>a</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0038ee8b-3f57-5838-a201-509d4bcd1c06</td>\n      <td>0</td>\n      <td>regular</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0048f974-e081-54ee-b0c4-e30b5f66c763</td>\n      <td>6</td>\n      <td>a</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Save the results in a dataframe with column name id,token_index,token\n",
+    "df = pd.DataFrame(all_masked_words, columns=['id', 'token_index', 'token'])\n",
+    "df.head()"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "## Save the dataframe as a csv file"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "outputs": [],
+   "source": [
+    "df.to_csv('sample_result.csv', index=False)"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}