hand_on_bert_QA.ipynb

{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nFXUJ4yjCP9V","executionInfo":{"status":"ok","timestamp":1651994430399,"user_tz":-120,"elapsed":45484,"user":{"displayName":"Mr. Z","userId":"12849447431415807254"}},"outputId":"4cb290d4-f7e1-4dc6-8523-6eedb3cb2b41"},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting transformers\n","  Downloading transformers-4.18.0-py3-none-any.whl (4.0 MB)\n","\u001b[K     |████████████████████████████████| 4.0 MB 4.6 MB/s \n","\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n","Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.11.3)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.64.0)\n","Requirement already satisfied: requests in 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/root/.cache/pip/wheels/87/39/dd/a83eeef36d0bf98e7a4d1933a4ad2d660295a40613079bafc9\n","Successfully built sacremoses\n","Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, transformers\n","  Attempting uninstall: pyyaml\n","    Found existing installation: PyYAML 3.13\n","    Uninstalling PyYAML-3.13:\n","      Successfully uninstalled PyYAML-3.13\n","Successfully installed huggingface-hub-0.5.1 pyyaml-6.0 sacremoses-0.0.53 tokenizers-0.12.1 transformers-4.18.0\n","Mounted at ./mydata\n"]}],"source":["!pip install transformers\n","from google.colab import drive\n","drive.mount('./mydata')\n","import sys\n","sys.path.append('./mydata/MyDrive/Colab Notebooks')"]},{"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","import torch\n","from transformers import BertForQuestionAnswering\n","from transformers import BertTokenizer"],"metadata":{"id":"npAnb_UiCvxg","executionInfo":{"status":"ok","timestamp":1651994443020,"user_tz":-120,"elapsed":9211,"user":{"displayName":"Mr. Z","userId":"12849447431415807254"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"43dvauseCP9a","executionInfo":{"status":"ok","timestamp":1651994704841,"user_tz":-120,"elapsed":7605,"user":{"displayName":"Mr. Z","userId":"12849447431415807254"}},"outputId":"327028ec-d9a6-487e-e1a1-b4d09ed59323"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   version                                               data\n","0        1  {'source': 'wikipedia', 'id': '3zotghdk5ibi9ce...\n","1        1  {'source': 'cnn', 'id': '3wj1oxy92agboo5nlq4r7...\n","2        1  {'source': 'gutenberg', 'id': '3bdcf01ogxu7zdn...\n","3        1  {'source': 'cnn', 'id': '3ewijtffvo7wwchw6rtya...\n","4        1  {'source': 'gutenberg', 'id': '3urfvvm165iantk..."],"text/html":["\n","  <div id=\"df-6fac949b-ae70-435d-a6ee-9d99f5771b62\">\n","    <div class=\"colab-df-container\">\n","      <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>version</th>\n","      <th>data</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>{'source': 'wikipedia', 'id': '3zotghdk5ibi9ce...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>{'source': 'cnn', 'id': '3wj1oxy92agboo5nlq4r7...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      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pd.read_json('coqa-dev-v1.0.json')\n","coqa.head()"]},{"cell_type":"code","execution_count":4,"metadata":{"id":"AEb3TEJfCP9c","executionInfo":{"status":"ok","timestamp":1651994711207,"user_tz":-120,"elapsed":6373,"user":{"displayName":"Mr. Z","userId":"12849447431415807254"}}},"outputs":[],"source":["### convert the whole thing into a one-trun QA setting\n","\n","del coqa[\"version\"]\n","#required columns in our dataframe\n","cols = [\"text\",\"question\",\"answer\"]\n","#list of lists to create our dataframe\n","comp_list = []\n","for index, row in coqa.iterrows():\n","    for i in range(len(row[\"data\"][\"questions\"])):\n","        temp_list = []\n","        temp_list.append(row[\"data\"][\"story\"])\n","        temp_list.append(row[\"data\"][\"questions\"][i][\"input_text\"])\n","        temp_list.append(row[\"data\"][\"answers\"][i][\"input_text\"])\n","        comp_list.append(temp_list)\n","\n","#saving the dataframe to csv file for further loading\n","new_df = pd.DataFrame(comp_list, columns=cols) \n","new_df.to_csv(\"CoQA_data.csv\", index=False)"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"02H2LKLYCP9d","executionInfo":{"status":"ok","timestamp":1651994714810,"user_tz":-120,"elapsed":1849,"user":{"displayName":"Mr. Z","userId":"12849447431415807254"}},"outputId":"11374dfa-e43a-4d88-ad1d-b574d86f53ec"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of question and answers:  108647\n"]}],"source":["# read the data again\n","data = pd.read_csv(\"CoQA_data.csv\")\n","print(\"Number of question and answers: \", len(data))"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["685fa84a89324932b00c9c90292647dd","26cd1f3b8ee84a518dfdab549684322b","8971353b70f140a4990690f5f04f69ea","93d5187dd796426880b07d70e787a2e2","3c868c623a74488bb1c2204cd955bf91","1cc895c70f0543ca9b00f1b171704d8e","a86fbc5ae40249e28bafeac68cd1f522","de5a4773e63747f0b1a300acb26555e4","8e88190bf7c447ccadb53400fbae447d","0fcf0c6733294aba867ecc987022d126","2cf9eec2212242b5b278c8302cf63cb6","65171fcd06b14c368c363a564bc47158","17b3e4a835d84e33aede809dc5edf9f5","7452051f9d4342f788326a9144ebafc6","b372705c1668473a8af03054d0d14778","11d3f83eeb68405794daec7f3a7d0082","f5764c3161f54149855c1c1d22ccca15","a44c7f2cf97c4135a4b73c97f512e6cc","b9c76e2234944c3aa7e1864f1a4a3785","b04696edde474a0b9a851a85fbfd4f0a","dc4b717c0cfe487684a4a66f1c0aa8e9","cd272168e03b45008d9ae7dd4a8ec976","357a20a0f82d4a949a818f66550e3503","a096cf7db4a44f579bcf605295877620","1b82d48d8a43486c8b74d37eac39db2d","bdc5be5863ab45a19fcfbc225502bb22","60d9a303e2c3468396829691e8d276f5","557a606e26294dd9b436bab5ee06702c","d481f5f8546244b9954a42932b4c6bd4","b6b213d88d414818812594911d337563","97f3419ca7e849459f6627a440d93604","8d73056ce0b44ee3ab07b47d89056ad2","9ff03c10591c404bbbf99a90a7ad90ce","6e6faa54e5274e37bd46cc5973861d0d","823e805c17fc456d90d385af9835a3dd","ebbf742e21834ba98e3f085b17fbd15c","a817754228604487a1cb6a7656af3724","bb1b65d038f24289a6ddca2df7e12e89","495a2e7e341240fb8d650bf498dda924","b4bfb06ae363470e9e17fa882d866394","95b498aa72b74f6eb8f47c9d1190074b","db414279b26f41cba5df9e06b15619ad","82888f74949e4bdeaf5d59c1d8afae9c","e42f5b134e7d4caea721d91ed58645a7"]},"id":"soJ_f5-hCP9e","executionInfo":{"status":"ok","timestamp":1651994762337,"user_tz":-120,"elapsed":44122,"user":{"displayName":"Mr. Z","userId":"12849447431415807254"}},"outputId":"7bd2dcaf-3ef0-4352-bd83-3c5eb5c10080"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/443 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"685fa84a89324932b00c9c90292647dd"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/1.25G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"65171fcd06b14c368c363a564bc47158"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/226k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"357a20a0f82d4a949a818f66550e3503"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6e6faa54e5274e37bd46cc5973861d0d"}},"metadata":{}}],"source":["# download the pretrained model\n","model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')\n","tokenizer = BertTokenizer.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')"]},{"cell_type":"code","execution_count":7,"metadata":{"id":"oIGq85mHCP9f","executionInfo":{"status":"ok","timestamp":1651994762338,"user_tz":-120,"elapsed":6,"user":{"displayName":"Mr. Z","userId":"12849447431415807254"}}},"outputs":[],"source":["def prediction(input_ids):\n","    # convert ids to tokens\n","    tokens = tokenizer.convert_ids_to_tokens(input_ids)\n","\n","    # for token, id in zip(tokens, input_ids):\n","    #     print('{:8}{:8,}'.format(token,id))\n","\n","    #first occurence of [SEP] token\n","    # tokenizer.sep_token_id is the id of the seperator token [SEP]\n","    sep_idx = input_ids.index(tokenizer.sep_token_id)\n","    # print(\"SEP token index: \", sep_idx)\n","    #number of tokens in segment A (question) - this will be one more than the sep_idx as the index in Python starts from 0\n","    num_seg_a = sep_idx+1\n","    # print(\"Number of tokens in segment A: \", num_seg_a)\n","    #number of tokens in segment B (text)\n","    num_seg_b = len(input_ids) - num_seg_a\n","    # print(\"Number of tokens in segment B: \", num_seg_b)\n","    #creating the segment ids as list consiting of 0s and 1s\n","    segment_ids = [0]*num_seg_a + [1]*num_seg_b\n","    #making sure that every input token has a segment id\n","    assert len(segment_ids) == len(input_ids)\n","\n","    #token input_ids to represent the input and token segment_ids to differentiate our segments - question and text\n","    output = model(torch.tensor([input_ids]),  token_type_ids=torch.tensor([segment_ids]))\n","\n","    #tokens with highest start and end scores\n","    answer_start = torch.argmax(output.start_logits)\n","    answer_end = torch.argmax(output.end_logits)\n","\n","    question = \" \".join(tokens[:(num_seg_a)])\n","    question = question.replace('[cls]','').replace('[sep]','')\n","    print(\"\\nQuestion:\\n{}\".format(question.capitalize()))\n","\n","    if answer_end >= answer_start:\n","        answer = \" \".join(tokens[answer_start:answer_end+1])\n","        print(\"Answer:\\n{}.\".format(answer.capitalize()))\n","    else:\n","        print(\"I am unable to find the answer to this question. Can you please ask another question?\")\n","        "]},{"cell_type":"markdown","source":["Try to attack the model on inputs (texts & questions)"],"metadata":{"id":"dNyg1Jbbj214"}},{"cell_type":"code","source":["# pick a random sample from the dataset\n","random_num = np.random.randint(0,len(data))\n","# or pick it by hand\n","random_num = 8575\n","\n","\n","question = data[\"question\"][random_num]\n","text = data[\"text\"][random_num]\n","\n","print('question: '+question,'\\n\\n','text: '+text,'\\n\\n','answer: '+data[\"answer\"][random_num])\n","\n","# concatenate as inputs to the model\n","input_ids = tokenizer.encode(question, text)\n","print(\"\\n The input has a total of {} tokens.\".format(len(input_ids)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1LySlXfaUUnH","executionInfo":{"status":"ok","timestamp":1651997191362,"user_tz":-120,"elapsed":341,"user":{"displayName":"Mr. Z","userId":"12849447431415807254"}},"outputId":"4aa7ee3a-a612-4e4c-9b74-d46580fef0eb"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["question: Was the newsstand busy all day? \n","\n"," text: CHAPTER II \n","\n","A VISIT TO THE NAVY-YARD \n","\n","There was a rush of business at the news-stand between twelve and one o'clock, but shortly after one this died away, and inside of half an hour Phil Newell told Walter that they might be on their way--\"If you are bound to enlist in Uncle Sam's service,\" he added. \n","\n","Walter made sure that the paper containing Job Dowling's permission for him to enter the navy was safe in his coat pocket, and then announced his readiness to depart. The owner of the stand called up Dan Brown and gave him a few directions, and in another minute Newell and Walter had boarded a Charlestown car and were off. \n","\n","\"I haven't been over to the navy-yard for several years,\" remarked Phil Newell, as they rode along. \"I used to know several of the boys that were there, but they've grown too old for the service. I reckon the yard is a busy place these days.\" \n","\n","And a busy place it proved to be as they turned into Chelsea Street, and moved along the solid granite wall which separates the yard from the public thoroughfare. From beyond came the creaking of hoists, and the ringing of countless hammers and anvils, for the government employees were hard at work, fitting out a warship or two and converting several private vessels into naval craft. \n","\n","\"I don't know if I'm just right about this,\" went on Phil Newell, as they headed for one of the numerous buildings near the wall, after being passed by a guard. \"It may be that they want to keep strangers out, now the war is on, and you'll have to go elsewhere to sign articles. But I know old Caleb Walton is here, and he'll tell me all he can, and set us straight.\"  \n","\n"," answer: no\n","\n"," The input has a total of 388 tokens.\n"]}]},{"cell_type":"code","source":["adversarial_text = text.replace('Budapest was two cities','Budapest was not one city')\n","#print(adversarial_text,'\\n\\n')\n","\n","adversarial_question = question.replace('always ','')#+' for what?'\n","adversarial_question = 'Which asian city is more expensive ?'\n","#print(adversarial_question)\n","\n","# tokenizer the question and text into word ids\n","# the tokens in question and text are seperated by '[SEP]' token\n","input_ids_t = tokenizer.encode(question, adversarial_text)\n","input_ids_q = tokenizer.encode(adversarial_question, text)"],"metadata":{"id":"Q41tGXHiUnE_","executionInfo":{"status":"ok","timestamp":1651997111357,"user_tz":-120,"elapsed":347,"user":{"displayName":"Mr. Z","userId":"12849447431415807254"}}},"execution_count":19,"outputs":[]},{"cell_type":"code","execution_count":20,"metadata":{"id":"u3oh-UruCP9i","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1651997130344,"user_tz":-120,"elapsed":15344,"user":{"displayName":"Mr. Z","userId":"12849447431415807254"}},"outputId":"57f33d5b-ff12-4c7e-ca54-33ddd0c59631"},"outputs":[{"output_type":"stream","name":"stdout","text":["\n","Question:\n","[cls] was budapest always one city ? [sep]\n","Answer:\n","Budapest became one city in 1872.\n","\n","Question:\n","[cls] was budapest always one city ? [sep]\n","Answer:\n","Budapest was not one city.\n","\n","Question:\n","[cls] which asian city is more expensive ? [sep]\n","Answer:\n","Tokyo.\n"]}],"source":["### run three predictions\n","prediction(input_ids)\n","prediction(input_ids_t)\n","prediction(input_ids_q)"]},{"cell_type":"markdown","source":["### Succeed example\n","\n","\n","1.   random_num = 75\n","\n","      adversarial_question = 'What is the second largest city in the US famous for ?'\n","\n","      *How to define this type of question?*\n","\n","\n"],"metadata":{"id":"IiwTeai0pKd3"}}],"metadata":{"interpreter":{"hash":"916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"},"kernelspec":{"display_name":"Python 3.8.10 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