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install transformers\n","!pip install datasets\n","import torch"]},{"cell_type":"code","source":["!unzip documents.zip"],"metadata":{"id":"-8rhcyCUDl3k"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import os\n","DOC_FOLDER = '/content/content/documents'\n","documents = os.listdir(DOC_FOLDER)\n","docs = []\n","doc_names = []\n","for doc in documents:\n","  with open(os.path.join(DOC_FOLDER,doc), 'r') as file:\n","    data = file.read().replace('\\n', ' ')\n","  docs.append(data)\n","  doc_names.append(doc)"],"metadata":{"id":"Y3XEzeS_Pi_p","executionInfo":{"status":"ok","timestamp":1676918606118,"user_tz":360,"elapsed":210,"user":{"displayName":"Salvi Rohan Charudatt","userId":"16799566509525298622"}}},"execution_count":25,"outputs":[]},{"cell_type":"code","source":["def segment_documents(docs, max_doc_length=450):\n","  # List containing full and segmented docs\n","  segmented_docs = []\n","\n","  for doc in docs:\n","    # Split document by spaces to obtain a word count that roughly approximates the token count\n","    split_to_words = doc.split(\" \")\n","\n","    # If the document is longer than our maximum length, split it up into smaller segments and add them to the list \n","    if len(split_to_words) > max_doc_length:\n","      for doc_segment in range(0, len(split_to_words), max_doc_length):\n","        segmented_docs.append( \" \".join(split_to_words[doc_segment:doc_segment + max_doc_length]))\n","\n","    # If the document is shorter than our maximum length, add it to the list\n","    else:\n","      segmented_docs.append(doc)\n","\n","  return segmented_docs"],"metadata":{"id":"Ib4lR_fJNEzd","executionInfo":{"status":"ok","timestamp":1676918198938,"user_tz":360,"elapsed":84,"user":{"displayName":"Salvi Rohan Charudatt","userId":"16799566509525298622"}}},"execution_count":18,"outputs":[]},{"cell_type":"code","source":["from sklearn.feature_extraction.text import TfidfVectorizer\n","from sklearn.metrics.pairwise import cosine_similarity\n","\n","def get_top_k_articles(query, docs, k=2):\n","\n","  # Initialize a vectorizer that removes English stop words\n","  vectorizer = TfidfVectorizer(analyzer=\"word\", stop_words='english')\n","\n","  # Create a corpus of query and documents and convert to TFIDF vectors\n","  query_and_docs = [query] + docs\n","  matrix = vectorizer.fit_transform(query_and_docs)\n","\n","  # Holds our cosine similarity scores\n","  scores = []\n","\n","  # The first vector is our query text, so compute the similarity of our query against all document vectors\n","  for i in range(1, len(query_and_docs)):\n","    scores.append(cosine_similarity(matrix[0], matrix[i])[0][0])\n","\n","  # Sort list of scores and return the top k highest scoring documents\n","  sorted_list = sorted(enumerate(scores), key=lambda x: x[1], reverse=True)\n","  top_doc_indices = [x[0] for x in sorted_list[:k]]\n","  top_docs = [docs[x] for x in top_doc_indices]\n","  \n","  return top_docs"],"metadata":{"id":"JI2MnM41NKZ_","executionInfo":{"status":"ok","timestamp":1676918203527,"user_tz":360,"elapsed":2419,"user":{"displayName":"Salvi Rohan Charudatt","userId":"16799566509525298622"}}},"execution_count":19,"outputs":[]},{"cell_type":"code","source":["from transformers import BertTokenizer, BertForQuestionAnswering\n","\n","model = BertForQuestionAnswering.from_pretrained('bert-large-uncased-whole-word-masking-finetuned-squad')\n","tokenizer = 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Rohan Charudatt","userId":"16799566509525298622"}},"outputId":"b835b643-1d8f-443a-a0e4-9c6b6f123bf9"},"execution_count":20,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading (…)lve/main/config.json:   0%|          | 0.00/443 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2502003ac3d54cc98ba21ada7c8da3e2"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)\"pytorch_model.bin\";:   0%|          | 0.00/1.34G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8bdd3cafc1b14cd59bda6d89d74e7212"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"63e306c6e2a94534a166086088290e04"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"58d9571e81cd46b79c0b9cf31f19a336"}},"metadata":{}}]},{"cell_type":"code","source":["def answer_question(question, answer_text):\n","\n","    input_ids = tokenizer.encode(question, answer_text, max_length=512)\n","    \n","    # ======== Set Segment IDs ========\n","    # Search the input_ids for the first instance of the `[SEP]` token.\n","    sep_index = input_ids.index(tokenizer.sep_token_id)\n","\n","    # The number of segment A tokens includes the [SEP] token istelf.\n","    num_seg_a = sep_index + 1\n","\n","    # The remainder are segment B.\n","    num_seg_b = len(input_ids) - num_seg_a\n","\n","    # Construct the list of 0s and 1s.\n","    segment_ids = [0]*num_seg_a + [1]*num_seg_b\n","\n","    # There should be a segment_id for every input token.\n","    assert len(segment_ids) == len(input_ids)\n","\n","    outputs = model(torch.tensor([input_ids]), # The tokens representing our input text.\n","                    token_type_ids=torch.tensor([segment_ids]), # The segment IDs to differentiate question from answer_text\n","                    return_dict=True) \n","\n","    start_scores = outputs.start_logits\n","    end_scores = outputs.end_logits\n","\n","    # ======== Reconstruct Answer ========\n","    # Find the tokens with the highest `start` and `end` scores.\n","    answer_start = torch.argmax(start_scores)\n","    answer_end = torch.argmax(end_scores)\n","\n","    # Get the string versions of the input tokens.\n","    tokens = tokenizer.convert_ids_to_tokens(input_ids)\n","\n","    # Start with the first token.\n","    answer = tokens[answer_start]\n","\n","    # Select the remaining answer tokens and join them with whitespace.\n","    for i in range(answer_start + 1, answer_end + 1):\n","        \n","        # If it's a subword token, then recombine it with the previous token.\n","        if tokens[i][0:2] == '##':\n","            answer += tokens[i][2:]\n","        \n","        # Otherwise, add a space then the token.\n","        else:\n","            answer += ' ' + tokens[i]\n","\n","    print('Answer: \"' + answer + '\"')"],"metadata":{"id":"3mg86qzJNPL0","executionInfo":{"status":"ok","timestamp":1676918236336,"user_tz":360,"elapsed":30,"user":{"displayName":"Salvi Rohan Charudatt","userId":"16799566509525298622"}}},"execution_count":21,"outputs":[]},{"cell_type":"code","source":["def get_slides(candidate_docs,doc_names,docs):\n","  result = []\n","  for cand_doc in candidate_docs:\n","    for j in range(0,len(docs)):\n","      if cand_doc == docs[j]:\n","        result.append((doc_names[j].split('_')[0],doc_names[j].split('_')[1].split('.')[0]))\n","  \n","  return result\n","    \n"],"metadata":{"id":"o-QVwf-9I2Ie","executionInfo":{"status":"ok","timestamp":1676918843565,"user_tz":360,"elapsed":87,"user":{"displayName":"Salvi Rohan Charudatt","userId":"16799566509525298622"}}},"execution_count":26,"outputs":[]},{"cell_type":"code","source":["# Enter our query here\n","query = \"What is 2's complement?\"\n","#query = \"What else does the bassist for Death From Above play?\"\n","#query = \"What projects is Jesse Keeler involved in?\"\n","\n","# Segment our documents\n","segmented_docs = segment_documents(docs, 450)\n","\n","# Retrieve the top k most relevant documents to the query\n","candidate_docs = get_top_k_articles(query, segmented_docs, 3)\n","# print(candidate_docs)\n","# # Return the likeliest answers from each of our top k most relevant documents in descending order\n","# for i in candidate_docs:\n","#   answer_question(query, i)\n","#   print (\"Reference Document: \", i)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IKz1BewuNRsG","executionInfo":{"status":"ok","timestamp":1676918490429,"user_tz":360,"elapsed":17389,"user":{"displayName":"Salvi Rohan Charudatt","userId":"16799566509525298622"}},"outputId":"3aad2949-36ea-4cc3-f0fd-adb9289a00c1"},"execution_count":24,"outputs":[{"output_type":"stream","name":"stdout","text":["['2’s Complement is 1’s Complement Plus One!  Again for N=5: 11111 @*%-1) (answer) + 1 The first step is trivial: replace 0 with 1, and 1  with 0. The result ((2% — 1) — K) is called the 1’s complement of K.  Adding 1 more gives the 2’s complement.      \\x0c', 'What about 2’s Complement?  How do we convert N-bit 2’s complement to (N+k)-bit 2’s complement (for k > 0)? For non-negative values,  °2’s complement is the same as unsigned (with an extra 0 for the sign) °So add k more leading 0s.  What about negative values?       \\x0c', 'Distinguish 2’s Complement from Negation  Here or elsewhere, you will hear the phrase “take the 2’s complement.”  We will try not to use “2’s complement” in that way.  Students get confused between the  2’s complement representation for signed integers and the operation of negation on a bit pattern for a number represented with 2’s complement.  For clarity, we suggest that you do the same.         \\x0c']\n","Answer: \"1 ’ s complement plus one\"\n","Reference Document:  2’s Complement is 1’s Complement Plus One!  Again for N=5: 11111 @*%-1) (answer) + 1 The first step is trivial: replace 0 with 1, and 1  with 0. The result ((2% — 1) — K) is called the 1’s complement of K.  Adding 1 more gives the 2’s complement.      \f\n","Answer: \"the same as unsigned\"\n","Reference Document:  What about 2’s Complement?  How do we convert N-bit 2’s complement to (N+k)-bit 2’s complement (for k > 0)? For non-negative values,  °2’s complement is the same as unsigned (with an extra 0 for the sign) °So add k more leading 0s.  What about negative values?       \f\n","Answer: \"negation\"\n","Reference Document:  Distinguish 2’s Complement from Negation  Here or elsewhere, you will hear the phrase “take the 2’s complement.”  We will try not to use “2’s complement” in that way.  Students get confused between the  2’s complement representation for signed integers and the operation of negation on a bit pattern for a number represented with 2’s complement.  For clarity, we suggest that you do the same.         \f\n"]}]},{"cell_type":"code","source":["op = get_slides(candidate_docs,doc_names,docs)\n","op"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hNRaVB6JJvKG","executionInfo":{"status":"ok","timestamp":1676918874983,"user_tz":360,"elapsed":67,"user":{"displayName":"Salvi Rohan Charudatt","userId":"16799566509525298622"}},"outputId":"a1739827-b97a-469e-c9ed-0e610226a459"},"execution_count":27,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[('006', 'Slide13'), ('006', 'Slide21'), ('006', 'Slide14')]"]},"metadata":{},"execution_count":27}]}]}