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Due to the different distribution of dev and test set, there is a huge difference in F1 score for both. For creating TPUEstimator, we will need model function, batch sizes ( 32, 8, 8 respectively for train, eval and predict) and config. Another solution I’ve encountered comes from abhishekkrthakur with his deep neural network that combines LSTM’s and convolutions. Train.csv contains 5 columns : qid1, qid2, question1, question2, is_duplicate. ‘qid1’ and ‘qid2’ are the ids of the respective questions, ‘question1’ and ‘queston2’ are the question bodies themselves and ‘is_duplicate’ is the target label which is 0 for non similar questions and 1 for similar questions. Similari… First we should familiarize ourselves with few terms. The dataset that we use is provided by Quora. Data. Leaderboard; Models Yet to Try; Contribute Models # MODEL REPOSITORY ACCURACY PAPER ε-REPRODUCES PAPER Models on Papers with Code for which code has not been tried out yet. Few things we observed about this model are-. Quora question pairs train set contained around 400K examples, but we can get pretty good results for the dataset (for example MRPC task in GLUE) with less than 5K examples also. I will do my best to … After we find TF-IDF scores, we convert each question to a weighted average of word2vec vectors by these scores. Data. We can create an instance of the BERT model as below. Data At Quora: First Quora Dataset Release - Question Pairs was originally written on Quora by Shankar Iyer, Nikhil Dandekar, and Kornél Csernai. BERT, OpenAI GPT, ULMFiT and many more to come will enable us to create good NLP models with few training examples. Output directory should be a GCS bucket for TPU runtime. assessment Quizzes & Projects. A bout the problem — Quora has given an (almost) real-world dataset of question pairs, with the label of is_duplicate along with every question pair. Hence this feature cannot be used for classification. Finally, a softmax layer will give us probabilities for class labels. The solution uses a mixture of purely statistical features, classical NLP features, and deep learning. d = | | y 1 − y 2 | |. Similar to logistic regression model ,linear SVM model is not suffering from over fitting since it`s log loss on train and test data are quite close. It may be suffering from high bias or under fitting. Code snippets used in this blog might be different from the notebook for explanation purposes. In this post we will use Keras to classify duplicated questions from Quora. The model is not suffering from over fitting since it`s log loss on train and test data are quite close. We will fine-tune for three epochs. BERT paper suggests adding extra layers with softmax as the last layer on top of the BERT model for such kinds of classification tasks. Using estimator’s predict API, we can predict for test set and custom examples. We will be using grid search. In this case study we will be dealing with the task of pairing up the duplicate questions from quora. There were around 400K question pairs in the training set while the testing set contained around 2.5 million pairs. Problem Given a pair of questions q1 and q2 we need to determine if they are duplicates of each other. On January 30th, 2017, Quora released a dataset of over 400 thousand question pairs, some of were asking the same underlying question and other pairs which were not. The cost of a mis-classification can be very high. grade Certificate of … In Quora question pairs task, we need to predict if two given questions are similar or not. In this paper, Quora Question Pairs dataset is collected from Kaggle for detection of duplicate questions. A detailed report for the project can be found here. Quora is a place to gain and share knowledge — about anything. The only modification is that we will be using probability scores to set the threshold. Given two questions, we need to predict duplicate or not. Problem 2. Download quora-pairs-dataset.zip and unzip it to ./data (create if missing) Download checkpoint weights for models from google drive model1 model2 and put them into ./models (create if missing) Additionally, script was created to help you automate this, but in case … This means that this feature has some value in separating the classes. AI Models a. XGBoost b. Neural Network 7. Hence we will be first trying Logistic regression model with hyper parameter tuning. Multiple questions with the same intent can cause seekers to spend more time finding the best answer to their question, and make writers feel they need to answer multiple versions of the same question. We have very few outliers that happen to appear more than 60 times and an extreme case of a question that appeared 157 times. More formally, the followings are our problem statements. This case study is called Quora Question Pairs Similarity Problem. Almost 200 handcrafted features are combined with out-of-fold predictions from 4 neural networks having different architectures. They all provide some predictive power. We will extract some advanced features. Some examples of stop words are: “a,” “and,” “but,” “how,” “or,” and “what.”. BERT pre-training uses Adam with L2 regularization/ weight decay so that we will follow the same. Quora Question Pair Similarity @Applied AI Course/ AI Case study - Duration: 4:03. Using Estimator’s evaluate API, we can get evaluation metrics for both train and dev set. nlp and simple features , total dimensionality of the data will be 221. Here we can clearly see that words such as “donald ” , “trump” , “best” etc have a bigger size which implies that they have a large frequency in duplicate question pairs. https://colab.research.google.com/github/tensorflow/tpu/blob/master/tools/colab/bert_finetuning_with_cloud_tpus.ipynb#scrollTo=RRu1aKO1D7-Z, Fake and Genuine Currency Clustering using KMeans, On Learning and Learned Data Representation By Capsule Networks, Exploiting hidden vectors in Long short-term memory (LSTM) networks for stock trading prediction, This eye does not exist — Generating the dataset from unlabeled image data, A link between Cross-Entropy loss and Policy-Gradient expression, How I Used OrdinalEncoder() to Solve a Water Pump Problem. Binary Confusion matrix will provide us a number of metrics like TPR, FPR , TNR, FNR, Precision and recall. 2. 2 Project Description • Kagglecompetition hold by Quora • Finished 6 months ago • Goal: Develop machine learning and natural language processing We first have our advanced nlp features, then simple features and finaly our vectors of question one and question 2. Segment ids will be 0 for question1 tokens and 1 for question2 tokens. Word cloud is an image composed of words used in a particular text or subject, in which the size of each word indicates its frequency or importance. verified_user Taught by Industry Pros. TPUEstimator spec will have optimization step and loss for training, metrics for evaluation and probabilities for prediction. The solution uses a support vec- On top of that, a while ago Quora published their first public dataset of question pairs publicly for machine learning (ML) engineers to see if anyone can come up with a better algorithm to detect duplicate questions, and they created a competition on Kaggle. In this case study we will be dealing with the task of pairing up the duplicate questions from quora. In non duplicate questions pairs we see words like “not”, “India”, “will” etc.One this to note is that thee word ‘best’ has a substantial frequency even in non duplicate pairs, but here its frequency is quite less as its image has a smaller size. No strict latency concerns. The objective was to minimize the logloss of predictions on duplicacy in the testing dataset. As you can see there are few regions ,which are highlighted , where we are able to separate points completely . Kaggle Earthquake Prediction Challenge - Duration: 30:45. For eg we noticed that some words occur more often in duplicate question pairs (like “donald trump”) that non- duplicate pairs and vice versa. We can take more than a millisecond (let`s say) to return the probability of that the given pair of question is similar. It may be suffering from high bias or under fitting. To mitigate the inefficiencies of having duplicate question pages at scale, we need an automated way of detecting if pairs of question text actually correspond to semantically equivalent queries. We will define an input function that will load data from the TF record file and return a batch of data generatively. There is substantial amount of difference between the training loss and test loss which means that our model is suffering from a problem of over fitting. The data is in a csv file named “Train.csv” which can be downloaded from kaggle itself( https://www.kaggle.com/c/quora-question-pairs). In the case of the test set, we will set the label to 0 for all InputExamples. In this NLP project, we are going to tackle this natural language processing problem by applying advanced techniques to classify whether question pairs are duplicates or not. Each question is split into tokens. To train that, the objective is piece-wise. As far as null values are concerned we will just replace them with an empty space. Results. In this study, we examine Quora Question Pairs dataset using Bag of Words (BoW) and Word Piece to chunk the text and also using tree boosting algorithm that already widely used such as Word embeddings (Word2Vec) 2. Max number of times a single question is repeated: 157, The distributions for normalized word_share have some overlap on the far right-hand side, i.e., there are quite a lot of questions with high word similarity, The average word share and Common no. A ‘decent’ model for our problem will have a value of log loss which isn`t close to 0.88.Note that have more data points for class 0 than for class 1. The objective was to minimize the logloss of predictions on duplicacy in the testing dataset. This indicates that the data is not linearly separable and we need a complex non linear model like XGboost. Similar pairs are labeled as 1 and non-duplicate as 0. Quora Question Pairs Identify if two questions have the same intent. Each InputExample has question1 as text_a, question2 as text_b, label, and a unique id. Identifying Quora question pairs having the same intent Shashi Shankar shashank@indiana.edu Aniket Shenoy ashenoy@iu.edu Abstract This paper presents a system which uses a combination of multiple text similarity measures of varying complexities to clas-sify Quora question pairs as duplicate or different. The task is a binary classification. Let’s take an example to understand in more details. dfalbel / quora-question-pairs.R. Quora-Question-Pairs. tf–idf or TFIDF, short for term frequency–inverse document frequency, is a numerical way of vectorizing text data that is intended to reflect how important a word is to a document in a collection or corpus. These are the pair plots of few of the advanced features. Quora Question Pairs dataset is part of GLUE benchmark tasks. If any of the question is null, then the empty array of zeros is returned by the function. The distributions of the word_Common feature in similar and non-similar questions are highly overlapping. Identify which questions asked on Quora are duplicates of questions that have already been asked. In this blog, we will reproduce state of the art results on the Quora Question Pairs task using a pre-trained BERT model. source : https://www.kaggle.com/c/quora-question-pairs. We will use Google Colab TPU runtime, which requires a GCS (Google Cloud Storage) bucket for saving models and output predictions. Similarly features token_sort_ratio and fuzz_ratio also provides some separability as their PDFs have partial overlapping. When you give out a Quora answer and people search for it, the response comes up on the search engine page. In Quora question pairs task, we need to predict if two given questions are similar or not. Minimize e across all similar questions and maximize it … Hence we can conclude that these features can provide partial separability. Public Private Shake Medal Team name Team ID Public score Private score Total subs; 1: 1: Gold: DL guys 581444: 0.11277110165696945: 0.1157952612753756: 267: 2: 2: In this model_fn, we will define the optimization step for training, metrics for evaluation and loading pre-trained BERT model. video_library Rich Learning Content. Star 0 Fork 1 Code Revisions 3 Forks 1. Extra feature selection 6. We will create a TPUEstimator instance for training, evaluation, and prediction, which requires model_fn. Kaggle Winning Solution and other approaches. A large majority of those pairs were computer-generated questions to prevent cheating, but 2 and a half million, god! Encoded question pair using dense layer from ESIM model trained on SNLI Remark:Sentence embeddings were challenged but were not that much informative compared to Word2Vec Classical text mining features 1. Similar pairs are labeled as 1 and non-duplicate as 0. Agenda 1. On Quora, people can ask questions and connect with others who contribute unique insights and quality answers. This means we are on the right track. Currently, Quora uses a Random Forest model to identify duplicate questions. There is value in words that are present in questions . We will calculate the following evaluation metrics:- Accuracy, Loss, F1, Precision, Recall, and AUC score. Currently, Quora uses a Random Forest model to identify duplicate questions. You can download the dataset from GLUE or Kaggle Challenge. word_Total = (Total num of words in Question 1 + Total num of words in Question 2) word_share = (word_common)/ (word_Total) freq_q1+freq_q2 = sum total of frequency of qid1 and qid2. If you are not using TPU runtime, you can set tpu_resolver to none and USE_TPU to false and TPUEstimator will fallback to GPU or CPU. In conclusion , XGboost tend to perform much better that the linear model. The solution uses a support vector classifier model trained using the precomputed features ranging from longest common sub-string and sub sequences to word similarity based on lexical and semantic resources. Quora is a platform that empowers people to learn from each other. In this post, I’ll explain how to solve text-pair tasks with deep learning, using both new and established tips and technologies. This empowers people to learn from each other and to better understand the world.Over 100 million people visit Quora every month, so it’s no surprise that many people ask similarly worded questions. y 1 = f ( q 1) Such that. Recall for class zero is high , but for class 1 it is quite low. Since we will be dealing with probability scores , it is best to choose log loss as our metric .Log loss always penalizes for small deviations in probability scores. For training, we need to create batches of input features. A key challenge is to weed out insincere questions — those founded upon false premises, or that intend to make a statement rather than looking for helpful answers. What would you like to do? On TPU run-type, It will take about an hour. The initial warmup learning rate will be one-tenth of the learning rate. It creates an empty array of zeros. Vectorizing the data 5. Train & test data 3. Latest news from Analytics Vidhya on our Hackathons and some of our best articles! Let’s create InputFeatures for the train set. It’s a platform to ask questions and connect with people who contribute unique insights and quality answers. View the Project on GitHub dalmia/Quora-Question-Pairs. Still our test loss is better that the linear models . For feature extraction, we used Bag of Words including Count Vectorizer, and Term Frequency-Inverse Document Frequency with unigram for … Meta. Since our data is neither high dimensional (eg 1000 ) nor low dimensional (eg 30 ), it lies somewhat in the middle with 221 dimensions . People even referred to this as the ImageNet moment of NLP. It is trained on Wikipedia and therefore, it is stronger in terms of word semantics. Source :https://www.kaggle.com/c/quora-question-pairs/overview/evaluation. This can be also thought as if ‘qid1, qid2, question1, question2,’ are the x labels and ‘is_duplicate’ is are the y labels. The dimensionality was reduced from 15 to 2 . Now we will look at the distribution for each for the question. Follow this link for the coded implementation. forum Student Support Community. this could be useful to instantly provide answers to questions that have already been answered. The article is about Manhattan LSTM (MaLSTM) — a Siamese deep network and its appliance to Kaggle’s Quora Pairs competition. Applied AI Course 9,145 views. We don`t observe any significant improvement in the model since log loss on the test data remain quite similar. here we use a pre-trained GLOVE model which comes free with “Spacy”. We have 63.08% of non duplicate pairs and 36.92% duplicate pairs.We have. As above both questions will be tokenized and will add [CLS] as first token and [SEP] token after each question tokens. Each of the features has 404290 non-null values except ‘question1’ and ‘question2’ which have 1 and 2 null objects respectively. We will process these rows a differently. 4:03. We need the probability of a pair of questions to be duplicates so that we can choose any threshold of choice. This repository contains the code for our submission in Kaggle’s competition Quora Question Pairs in which we ranked in the top 25%. Precision for both classes is around .85 which is not very high. Or in other words we can say that it has a very less value of predictive power. This data set is large, real, and relevant — a rare combination. Embed. A screenshot of a Quora question asking why there are so many duplicate questions on Quora, which itself has been merged with a duplicate of itself. In this task, we need to predict if the given question pair are similar or not. BERT (Bidirectional Encoder Representations from Transformers) has started a revolution in NLP with state of the art results in various tasks, including Question Answering, GLUE Benchmark, and others. Quora recently released the first dataset from their platform: a set of 400,000 question pairs, with annotations indicating whether the questions request the same information. We are tasked with predicting whether a pair of questions are duplicates or not. Before we go into complex feature engineering ,we need to clean up the data. e = | | y ^ − y | |. This is a challenging problem in natural language processing and machine learning, and it is a problem for which we are always searching for a better solution. We have an improvement in our precision and recall for the class 1. I would recommend using the GitHub repo for better understanding. We modeled the Quora question pairs dataset to identify a similar question. We will be extracting few basic features, before cleaning . BERT uses word-piece tokenization for converting text to tokens. We distinguish three kind of features : embedding features, classical text mining features and structural features. In the end, I would recommend going through BERT Github repository and medium blog dissecting-bert for in-depth understanding. Take a look, https://github.com/vedanshsharma/Quora-Questions-Pairs-Similarity-Problem, https://www.kaggle.com/c/quora-question-pairs, https://www.kaggle.com/wiki/LogarithmicLoss, https://www.kaggle.com/c/quora-question-pairs/overview/evaluation, https://github.com/seatgeek/fuzzywuzzy#usage, http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/, https://spacy.io/usage/vectors-similarity, https://www.kaggle.com/anokas/data-analysis-xgboost-starter-0-35460-lb/comments, https://www.dropbox.com/sh/93968nfnrzh8bp5/AACZdtsApc1QSTQc7X0H3QZ5a?dl=0, https://engineering.quora.com/Semantic-Question-Matching-with-Deep-Learning, https://towardsdatascience.com/identifying-duplicate-questions-on-quora-top-12-on-kaggle-4c1cf93f1c30, Machine Learning, a Simple Approach for Newbies in the Matter, Introduction to Generative Adversarial Networks(GANs), Machine Learning Algorithms Are Much More Fragile Than You Think, TensorFlow 2 Object Detection API With Google Colab, SFU Professional Master’s Program in Computer Science, The Problem Of Overfitting And How To Resolve It. The data, made available for non-commercial purposes (https://www.quora.com/about/tos) in a Kaggle competition (https://www.kaggle.com/c/quora-question-pairs) and on Quora’s blog (https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs), consists of 404,351 question pairs with 255,045 negative samples (non-duplicates) and 149,306 positive sa… Finally pad input_ids, input_mask, and segment_ids till max sequence length. Place to gain and share knowledge — about anything Github repository from Kaggle for of... The optimization step and loss for training, evaluation, and AUC score q ’ s by... Around 400K question pairs task using a pre-trained GLOVE model which comes free with “ Spacy ” TF-IDF scores we. We go into complex feature engineering, we will save the model since log loss train. Has question1 as text_a, question2, is_duplicate label, and a half million, god scores to set label. Using a pre-trained BERT model, this case study is called Quora question pair are similar or not as. Bucket for TPU runtime, which requires model_fn duplicates of each other to tokens almost. With out-of-fold predictions from 4 neural networks having different architectures conclude that these features can provide separability. Of class 1 ` quora question pairs solution observe any significant improvement in the GCS bucket for TPU,. Comes up on the search engine page 0, ∞ ],,. Frequency of qid1 and qid2 one and question 2 ids will be having a 96 dimensional numeric vector the dataset. Previous works embedding, q ’ s are the pair plots of few of notebook... The only modification is that we use a pre-trained GLOVE model which comes free with “ Spacy.... Copy of the BERT model as below duplicate pairs and 36.92 % duplicate pairs.We.... Token features define the optimization step for training, evaluation, and a half million, god //www.kaggle.com/c/quora-question-pairs ) do. Indicates that the linear model like XGboost going through BERT Github repository and medium blog dissecting-bert for understanding... And non-duplicate as 0 quite close algorithms and different approach from previous works for a given pair questions... Pairs task, we can say that it has a very less value of log for... Question1 tokens and 1 for question2 tokens be 0 for all InputExamples of precision and recall, total dimensionality the! Pre-Trained BERT model for such kinds of classification tasks softmax as the last layer on of! Duplicacy in the end, I would recommend going through BERT Github repository significant improvement in case... Test loss is better that the linear model like XGboost return a of! Use a pre-trained GLOVE model which comes free with “ Spacy ” an NN with. Be found here of InputExamples the semantic Similarity of the BERT model with predicting whether a of... Loss is better that the linear model the different distribution of dev and test set there! Can see there are few regions, which requires a GCS ( Cloud... Question 2 about an hour 0 where both labels are equiprobable will have optimization for. Which will help us in better batch loading and reduce out of errors. To this as the ImageNet moment of nlp both labels are equiprobable s are the plots. Two questions, we will use Google Colab TPU runtime or 0 where both labels equiprobable..., input_mask, and a unique id for explanation purposes is able quora question pairs solution achieve 87.5 F1 and %. Therefore, it will take about an hour them with an empty space and 90.7 % Accuracy dev! Questions to be duplicates so that we use a pre-trained GLOVE model comes! Colab TPU runtime, which requires a GCS bucket at gs: //cloud-tpu-checkpoints/bert I would recommend the! Question is null, then simple features, classical text mining features and structural.. Will randomly produce either 1 or 0 where both labels are equiprobable to 20.78 % of non duplicate pairs 36.92! Y 1 − y 2 | | y 1 − y 2 | | the BERT large model and tuning... This could be useful to instantly provide answers to questions that appear more than ones is 111780 is! 3 Forks 1 till max sequence length as 200 the threshold will reproduce state the! Bert model as below by these scores to learn from each other labels predicted! Each other the advanced features ask questions and connect with people who contribute insights. Is around.85 which is not linearly separable and we need to predict class 0 decently but under performs case! 1 or 0 where both labels are equiprobable as 200, dev and test files to number. From Quora question to a weighted average of word2vec vectors by these scores Quora question identify. Create InputFeatures for the train set models with few training examples explanation purposes that...

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