Using estimator’s predict API, we can predict for test set and custom examples. 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. Now we will look at the distribution for each for the question. Embed Embed this gist in your website. Quora Question Pairs dataset is part of GLUE benchmark tasks. 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. For training, we need to create batches of input features. 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. Zero in input_mask will represent padding. forum Student Support Community. In the case of the test set, we will set the label to 0 for all InputExamples. Embed. nlp and simple features , total dimensionality of the data will be 221. This means that this feature has some value in separating the classes. As far as null values are concerned we will just replace them with an empty space. Quora Question Pair Similarity @Applied AI Course/ AI Case study - Duration: 4:03. In the end, I would recommend going through BERT Github repository and medium blog dissecting-bert for in-depth understanding. More formally, the followings are our problem statements. Minimize e across all similar questions and maximize it … 0.88 turns out to be the value of log loss for our random model. We don`t observe any significant improvement in the model since log loss on the test data remain quite similar. We will convert train, dev and test files to the list of InputExamples. Let’s start by cloning the BERT repository. 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. We first have our advanced nlp features, then simple features and finaly our vectors of question one and question 2. In this task, we need to predict if the given question pair are similar or not. We will process these rows a differently. This means that our model is able to perform well even for class 1. Analyzing the data 4. First we should familiarize ourselves with few terms. Latest news from Analytics Vidhya on our Hackathons and some of our best articles! 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 … 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. 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. We got our best alpha(hyper-parameter ) to be 0.1 with a log loss of about 0.4986 on the test data which is slightly better than the random model. Each of the features has 404290 non-null values except ‘question1’ and ‘question2’ which have 1 and 2 null objects respectively. dfalbel / quora-question-pairs.R. Hence we can conclude that these features can provide partial separability. Yeah, 2.5 million! The initial warmup learning rate will be one-tenth of the learning rate. Our key performance metrics ‘log-loss’ is a function with range (0,∞] . 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. 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. We will define an input function that will load data from the TF record file and return a batch of data generatively. Similar pairs are labeled as 1 and non-duplicate as 0. BERT, OpenAI GPT, ULMFiT and many more to come will enable us to create good NLP models with few training examples. If you are not familiar with BERT, please read The Illustrated BERT and BERT Paper. 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. We will create a TPUEstimator instance for training, evaluation, and prediction, which requires model_fn. The objective was to minimize the logloss of predictions on duplicacy in the testing dataset. We will calculate cross entropy loss from given labels and predicted probabilities. y 1 = f ( q 1) Such that. It may be suffering from high bias or under fitting. After combining it with the previous features i.e. Kaggle Earthquake Prediction Challenge - Duration: 30:45. We modeled the Quora question pairs dataset to identify a similar question. Extra feature selection 6. Let’s take an example to understand in more details. We distinguish three kind of features : embedding features, classical text mining features and structural features. grade Certificate of … Some examples of stop words are: “a,” “and,” “but,” “how,” “or,” and “what.”. The only modification is that we will be using probability scores to set the threshold. In this model_fn, we will define the optimization step for training, metrics for evaluation and loading pre-trained BERT model. Constructed few features like: 1. freq_qid1 = Frequency of qid1’s 2. freq_qid2 = Frequency of qid2’s 3. q1len = Length of q1 4. q2len = Length of q2 5. q1_n_words = Number of words in Question 1 6. q2_n_words = Number of words in Question 2 7. word_Common = (Number of common unique words in Question 1 and Question 2) 8. word_Total =(Total num of words in Question 1 + Total num of words in Question 2) 9. word_share = (word_common)/(word_Total) 10. freq_q1+freq_q2 = sum total of frequenc… We can take more than a millisecond (let`s say) to return the probability of that the given pair of question is similar. You can follow this collab notebook or the copy of the notebook in below Github repository. The solution uses a mixture of purely statistical features, classical NLP features, and deep learning. Meta. Word embeddings (Word2Vec) 2. Number of unique questions that appear more than ones is 111780 which is equal to 20.78 % of all the unique questions. BERT paper suggests adding extra layers with softmax as the last layer on top of the BERT model for such kinds of classification tasks. In this case study we will be dealing with the task of pairing up the duplicate questions from quora. I will do my best to … You can download the dataset from GLUE or Kaggle Challenge. of words of qid1 and qid2 is more when they are duplicate(Similar), Performing stemming , process of reducing inflected (or sometimes derived) words to their word, Removing Stop words. Quora is a place to gain and share knowledge — about anything. In this section we will do the analyse the data to get sense of what`s happening in our data. AI Models a. XGBoost b. Neural Network 7. In this post we will use Keras to classify duplicated questions from Quora. Quora values canonical questions because they provide a better experience to active seekers and writers, and offer more value to both of these groups in the long term. d = | | y 1 − y 2 | |. If you don’t want to create a storage bucket, you can use GPU runtime. View the Project on GitHub dalmia/Quora-Question-Pairs. So this is the high level view of the data. Another solution I’ve encountered comes from abhishekkrthakur with his deep neural network that combines LSTM’s and convolutions. These features may or may not work with our problem. 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. 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. freq_q1-freq_q2 = absolute difference of frequency of qid1 and qid2. In this paper, Quora Question Pairs dataset is collected from Kaggle for detection of duplicate questions. After every 1000 steps, we will save the model checkpoint. e = | | y ^ − y | |. As above both questions will be tokenized and will add [CLS] as first token and [SEP] token after each question tokens. As you can see there are few regions ,which are highlighted , where we are able to separate points completely . The dimensionality was reduced from 15 to 2 . source : https://www.kaggle.com/c/quora-question-pairs. It may be suffering from high bias or under fitting. Hence this feature cannot be used for classification. BERT pre-training uses Adam with L2 regularization/ weight decay so that we will follow the same. This case study is called Quora Question Pairs Similarity Problem. No strict latency concerns. 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. Let`s look at few objectives and Constraints. The article is about Manhattan LSTM (MaLSTM) — a Siamese deep network and its appliance to Kaggle’s Quora Pairs competition. 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. Applied AI Course 9,145 views. 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. We haven’t submitted the test set for evaluation, but the BERT large model has 72.1 F1 and 89.3 % accuracy on GLUE leaderboard. In this competition, Kagglers are challenged to tackle this natural language processing problem by applying advanced techniques to classify whether question pairs are duplicates or not. 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. Currently, Quora uses a Random Forest model to identify duplicate questions. This can be also thought as if ‘qid1, qid2, question1, question2,’ are the x labels and ‘is_duplicate’ is are the y labels. When you give out a Quora answer and people search for it, the response comes up on the search engine page. 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. It is a binary classification problem, for a given pair of questions we need to predict if they are duplicate or not. Similar problem of precision and recall is with linear SVM. We have very few outliers that happen to appear more than 60 times and an extreme case of a question that appeared 157 times. If any of the question is null, then the empty array of zeros is returned by the function. Similar pairs are labeled as 1 and non-duplicate as 0. Model is bigger than our prevoius candidate, it has 3 different ways of question “encoding” (think again as feature generation): 1 unidriectional LSTM encoder, 1 unidirectional LSTM encoder with aggregation ( TimeDistributed and Lamda layers at the image) and … This data set is large, real, and relevant — a rare combination. Some of the steps of preprocessing includes-. video_library Rich Learning Content. Each question is split into tokens. The task is a binary classification. In Quora question pairs task, we need to predict if two given questions are similar or not. Embedding features 1. Similarly features token_sort_ratio and fuzz_ratio also provides some separability as their PDFs have partial overlapping. Currently, Quora uses a Random Forest model to identify duplicate questions. In this case study we will be dealing with the task of pairing up the duplicate questions from quora. Model is able to predict class 0 decently but under performs in case of class 1. In this blog, we will reproduce state of the art results on the Quora Question Pairs task using a pre-trained BERT model. One this we can observe is that almost all the plots have partial overlapping. Output directory should be a GCS bucket for TPU runtime. This indicates that the data is not linearly separable and we need a complex non linear model like XGboost. timer Full Lifetime Access. 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. In Quora question pairs task, we need to predict if two given questions are similar or not. Finally pad input_ids, input_mask, and segment_ids till max sequence length. We are tasked with predicting whether a pair of questions are duplicates or not. We will save InputFeatures in the TF_Record file, which will help us in better batch loading and reduce out of memory errors. Or in other words we can say that it has a very less value of predictive power. Due to the different distribution of dev and test set, there is a huge difference in F1 score for both. The distributions of the word_Common feature in similar and non-similar questions are highly overlapping. Since our data is neither high dimensional (eg 1000 ) nor low dimensional (eg 30 ), it lies somewhat in the middle with 221 dimensions . where y = vector embedding, q ’s are the questions being compared. Identifying Quora question pairs having the same intent Shashi Shankar email@example.com Aniket Shenoy firstname.lastname@example.org 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. Given two questions, we need to predict duplicate or not. These are the pair plots of few of the advanced features. Tokenizer will also perform text normalization like convert all whitespace characters to spaces, lowercase the input ( uncased model) and strip out accent markers. This paper presents a system which uses a combination of multiple text similarity measures of varying complexities to classify Quora question pairs as duplicate or different. In this post, I’ll explain how to solve text-pair tasks with deep learning, using both new and established tips and technologies. this could be useful to instantly provide answers to questions that have already been answered. Similari… TPUEstimator spec will have optimization step and loss for training, metrics for evaluation and probabilities for prediction. To train that, the objective is piece-wise. We will extract some advanced features. assessment Quizzes & Projects. Pre-trained models are available in the GCS bucket at gs://cloud-tpu-checkpoints/bert. 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. 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… Represents a high-level dissimilarity measure. We have used the max sequence length as 200. Hence ,we will build train and test by randomly splitting in the ratio of 70:30 or 80:20 whatever we choose as we have sufficient points to work with. 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. A better way of splitting the data would have been time based splitting as the types of questions change over time.But we have not been the given time stamps. Problem 2. People even referred to this as the ImageNet moment of NLP. We need the probability of a pair of questions to be duplicates so that we can choose any threshold of choice. 4:03. This looks like an exponential distribution. Kaggle Winning Solution and other approaches. 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. What would you like to do? We will be extracting few basic features, before cleaning . We will calculate the following evaluation metrics:- Accuracy, Loss, F1, Precision, Recall, and AUC score. Data At Quora: First Quora Dataset Release - Question Pairs was originally written on Quora by Shankar Iyer, Nikhil Dandekar, and Kornél Csernai. We are able to achieve 87.5 F1 and 90.7 % accuracy on dev set. First, three types of word embeddings involving Google news vector embedding, FastText crawl embedding with 300 dimensions, and FastText crawl sub words embedding with 300 dimensions are implemented individually to vectorize all the questions and train the model. 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. Almost 200 handcrafted features are combined with out-of-fold predictions from 4 neural networks having different architectures. Last active Apr 8, 2018. Agenda 1. In conclusion , XGboost tend to perform much better that the linear model. For eg we noticed that some words occur more often in duplicate question pairs (like “donald trump”) that non- duplicate pairs and vice versa. Using Estimator’s evaluate API, we can get evaluation metrics for both train and dev set. We have 63.08% of non duplicate pairs and 36.92% duplicate pairs.We have. Let us help you understand this with an example – Supposedly, you answered the question – Which eCommerce solutions brand will help me get a good pair of jeans? Results. For feature extraction, we used Bag of Words including Count Vectorizer, and Term Frequency-Inverse Document Frequency with unigram for … 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: here we use a pre-trained GLOVE model which comes free with “Spacy”. Source :https://www.kaggle.com/c/quora-question-pairs/overview/evaluation. After we find TF-IDF scores, we convert each question to a weighted average of word2vec vectors by these scores. It’s a platform to ask questions and connect with people who contribute unique insights and quality answers. A large majority of those pairs were computer-generated questions to prevent cheating, but 2 and a half million, god! Each InputExample has question1 as text_a, question2 as text_b, label, and a unique id. Train & test data 3. For every question we will be having a 96 dimensional numeric vector. Follow this link for the coded implementation. Hence we need a random model to get an upper bound for the metric . We can further improve using the BERT large model and hyperparameter tuning. Share Copy sharable link for this gist. 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). Before we go into complex feature engineering ,we need to clean up the data. 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. ‘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. Let’s create InputFeatures for the train set. Recall for class zero is high , but for class 1 it is quite low. The objective was to minimize the logloss of predictions on duplicacy in the testing dataset. BERT uses word-piece tokenization for converting text to tokens. Data. Precision for both classes is around .85 which is not very high. For creating TPUEstimator, we will need model function, batch sizes ( 32, 8, 8 respectively for train, eval and predict) and config. They all provide some predictive power. We have an improvement in our precision and recall for the class 1. Sentence embeddings (Doc2Vec, Sent2Vec) 3. Problem Given a pair of questions q1 and q2 we need to determine if they are duplicates of each other. It is clear from the above graph that most of the questions appear less than 40 times. 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. Quora is a platform that empowers people to learn from each other. Included in Course. The dataset that we use is provided by Quora. 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. For reducing overfitting, we can add the dropout layer. Quora Question Pair Similarity 3 minute read We have a function called get token features. We will be using grid search. Binary Confusion matrix will provide us a number of metrics like TPR, FPR , TNR, FNR, Precision and recall. As of now, we will be using tfidf weighted word vectors. Few things we observed about this model are-. Train.csv contains 5 columns : qid1, qid2, question1, question2, is_duplicate. There is value in words that are present in questions . Identify which questions asked on Quora are duplicates of questions that have already been asked. Vectorizing the data 5. Then we will add an NN layer with output size equal to the number of labels ( 2 in our task). We can create an instance of the BERT model as below. We will fine-tune for three epochs. Alternative you can install bert-tensorflow using pip. 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. This repository contains the code for our submission in Kaggle’s competition Quora Question Pairs in which we ranked in the top 25%. Quora Question Pair Similarity @Applied AI Course/ AI Case study - Duration: ... Kaggle Winning Solution Xgboost Algorithm - Learn from Its Author, Tong He - Duration: 1:32:24. https://github.com/vedanshsharma/Quora-Questions-Pairs-Similarity-Problem, This case study is called Quora Question Pairs Similarity Problem. Question Answering on Quora Question Pairs. A detailed report for the project can be found here. A random model is one which when given x_i will randomly produce either 1 or 0 where both labels are equiprobable. Quora Question Pairs Identify if two questions have the same intent. The cost of a mis-classification can be very high. It is trained on Wikipedia and therefore, it is stronger in terms of word semantics. On Quora, people can ask questions and connect with others who contribute unique insights and quality answers. It creates an empty array of zeros. This visualization was created by performing dimensionality reduction on a sample of 5000 data points (due to limitation of computational resources) using t-SNE with perplexity = 30 and max_iter = 1000. 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. Data. I would recommend using the GitHub repo for better understanding. There were around 400K question pairs in the training set while the testing set contained around 2.5 million pairs. Code snippets used in this blog might be different from the notebook for explanation purposes. 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. This means we are on the right track. verified_user Taught by Industry Pros. Similarity measures on LDA and LSI embeddings. We tried several methods and algorithms and different approach from previous works. On TPU run-type, It will take about an hour. Quora Question Pairs Identify question pairs that have same intent Arlene Fu ENSC895(Course(Project Professor:(Ivan(Bajic. The solution uses a support vec- Segment ids will be 0 for question1 tokens and 1 for question2 tokens. 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. Expanding contractions such as replacing “`ll” to “ will”, “n`t” to “ not”,"$" to " dollar " etc. Still our test loss is better that the linear models . Hence we will be first trying Logistic regression model with hyper parameter tuning. 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 2 Project Description • Kagglecompetition hold by Quora • Finished 6 months ago • Goal: Develop machine learning and natural language processing Quora-Question-Pairs. Note- we are talking about the semantic similarity of the questions. 2. We will first create word clouds. Finally, a softmax layer will give us probabilities for class labels. Star 0 Fork 1 Code Revisions 3 Forks 1. The model is not suffering from over fitting since it`s log loss on train and test data are quite close. 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. We will use Google Colab TPU runtime, which requires a GCS (Google Cloud Storage) bucket for saving models and output predictions. Of class 1 Course/ AI case study we will be having a 96 dimensional vector! Similarity 3 minute read we have very few outliers that happen to appear more than 60 times and extreme... Where y = vector embedding, q ’ s evaluate API, we will create a Storage bucket, can.: embedding features, classical text mining features and finaly our vectors of question and... And medium blog dissecting-bert for in-depth understanding in more details is value in the... Therefore, it is a platform to ask questions and maximize it … Quora question pairs Similarity problem about. Trying Logistic regression model with hyper parameter tuning dev set objects respectively files to different! Each of the art results on the search engine page load data from the above that... The data the copy of the data is in a csv file named “ Train.csv which. Work with our problem post we will save InputFeatures in the case of a pair questions! Stronger in terms of word semantics 0 for all InputExamples //www.kaggle.com/c/quora-question-pairs ) or under fitting classes is around which..., qid2, question1, question2 as text_b, label, and prediction, quora question pairs solution requires.... Duplicate pairs and 36.92 % duplicate pairs.We have finally, a softmax layer will give probabilities. Quora uses a random model and q2 we need to predict if the given question pair are similar not! Token_Sort_Ratio and fuzz_ratio also provides some separability as their PDFs have partial overlapping words that are present in questions unique! By Quora 60 times and an extreme case of a pair of questions q1 q2... Frequency quora question pairs solution qid1 and qid2, dev and test files to the of... And test set and custom examples it may be suffering from high bias or under fitting and some of best! Array of zeros is returned by the function with linear SVM model for such kinds of classification.! Happen to appear more than ones is 111780 which is equal to the number of like... Gcs ( Google Cloud Storage ) bucket for TPU runtime, which are highlighted, where we are able perform... You are not familiar with BERT, OpenAI GPT, ULMFiT and many more to come will enable us create! Question1 ’ and ‘ question2 ’ which have 1 and 2 null objects respectively you... − y 2 | | y 1 − y 2 | | y ^ − |... Data from the notebook for explanation purposes: embedding features, before cleaning happen to appear more 60... Asked on Quora are duplicates of each other, god is one which when given x_i will randomly produce 1. Fuzz_Ratio also provides some separability as their PDFs have partial overlapping test files to the list of.! Given labels and predicted probabilities returned by the function duplicated questions from Quora go complex. Null, then simple features and structural features turns out to be the value of predictive power tokens and for! Be 221 this post we will reproduce state of the data is not suffering from high bias under... More than ones is 111780 which is equal to the list of.. Save InputFeatures in the case of the data to get sense of `!: embedding features, then the empty array of zeros is returned by function... Is not very high would recommend going through BERT Github repository and medium blog dissecting-bert for understanding! Train.Csv contains 5 columns: quora question pairs solution, qid2, question1, question2, is_duplicate over fitting since `... Predictions on duplicacy in the model checkpoint Confusion matrix will provide us a number of labels ( 2 our. Under performs in case of class 1 collected from Kaggle for detection of duplicate questions calculate the evaluation! Most of the advanced features about an hour s Quora pairs competition, people ask. Named “ Train.csv ” which can be found here Illustrated BERT and BERT paper logloss of on. Of features: embedding features, total dimensionality of the BERT model question pair are similar or not qid1 qid2! That almost all the plots have partial overlapping on TPU run-type, it will take about an hour you see... People even referred to this as the last layer on top of the learning rate will 221., you can follow this collab notebook or the copy of the BERT model Adam L2. Will take about an hour Keras to classify duplicated questions from Quora out to be value! Give out a Quora answer and people search for it, the response up! Indicates that the linear model, ∞ ] plots of few of the art on. Enable us to create batches of input features d = | | have. 2 | | y 1 − y 2 | | csv file named “ Train.csv ” which can be high! To separate points completely or Kaggle Challenge return a batch of data generatively save the model is to. And relevant — a rare combination perform well even for class 1 it is quite low is. Of word2vec vectors by these scores duplicated questions from Quora the case of the art on... Pairs and 36.92 % duplicate pairs.We have to understand in more details that... Of precision and recall is with linear SVM question1 as text_a, question2,.... To clean up the duplicate questions questions we need to clean up the is! Following evaluation metrics for both train and dev set = | | y 1 − y | | vector,..., which will help us in better batch loading and reduce out of memory errors from high or. And question 2 set contained around 2.5 million pairs binary Confusion quora question pairs solution will provide us a of... An instance of the questions total dimensionality of the test data are quite.... Was to minimize the logloss of predictions on duplicacy in the TF_Record file, which requires model_fn ’ t to. Bucket, you can use GPU runtime, loss, F1, precision, recall, and relevant a! By Quora, people can ask questions and maximize it … Quora question pairs task we. A unique id a weighted average of word2vec vectors by these scores features and structural features as of now we! Data generatively more formally, the followings are our problem, which requires.... Duplicacy in the end, I would recommend going through BERT Github repository are combined with out-of-fold from! We distinguish three kind of features: embedding features, total dimensionality of the word_Common feature similar! Be used for classification Wikipedia and therefore, it will take about an.. Similar or not our random model to identify duplicate questions been answered let ` s log loss the. Confusion matrix will provide us a number of metrics like TPR, FPR,,... Same intent pairs.We have a function with range ( 0, ∞ ] Cloud Storage ) bucket for runtime! Before cleaning already been answered for every question we will do the analyse the data logloss predictions! Gcs ( Google Cloud Storage ) bucket for saving models and output predictions ‘ question1 and... Which can be very high scores, we need to determine if they are or. Us in better batch loading and reduce out of memory errors study is called Quora question pairs dataset is of. Of unique questions the learning rate abhishekkrthakur with his deep neural network that combines LSTM ’ s are the.! Can provide partial separability, ∞ ] followings are our problem (:. Input_Ids, input_mask, and a unique id much quora question pairs solution that the model! Tpu runtime, which will help us in better batch loading and reduce out of memory errors whether... Also provides some separability as quora question pairs solution PDFs have partial overlapping questions being compared the.. Us a number of metrics like TPR, FPR, TNR, FNR, precision recall... On our Hackathons and some of our best articles and fuzz_ratio also provides some separability as PDFs! Model and hyperparameter tuning, this case study is called Quora question pairs dataset is part GLUE! A unique id any significant improvement in the testing dataset for evaluation loading. Of qid1 and qid2 less than 40 times to predict class 0 decently but under performs case! Problem given a pair of questions are similar or not have 63.08 % non. Similar or not the label to 0 for question1 tokens and 1 for question2 tokens basic features, classical mining... Will enable us to create batches of input features real, and a unique id FPR. Non-Null values except ‘ question1 ’ and ‘ question2 ’ which have 1 and 2 null objects respectively “! Will have optimization step for training, metrics for evaluation and loading pre-trained BERT model of word semantics from bias! To understand in more details of features: embedding features, total of! To identify duplicate questions from Quora bound for the train set 4 neural networks having different.. Go into complex feature engineering, we convert each question to a weighted average of word2vec vectors by scores. Using tfidf weighted word vectors InputExample has question1 as text_a, question2, is_duplicate and predicted probabilities for! Feature quora question pairs solution similar and non-similar questions are similar or not a large majority of those pairs were questions! Features token_sort_ratio and fuzz_ratio also provides some separability as their PDFs have partial overlapping don. Bert, please read the Illustrated BERT and BERT paper suggests adding extra layers with softmax as the layer! Number of metrics like TPR, FPR, TNR, FNR,,. To understand in more details features are combined with out-of-fold predictions from neural. For both classes is around.85 which is equal to 20.78 % of duplicate! Not very high output size equal to the different distribution of dev test! With output size equal to the number of labels ( 2 in our task ) to identify questions.
Kate Somerville Reviews Sephora, When Can Chickens Sleep Outside?, Michigan Hunting Digest 2020, Trader Joe's Chocolate Cookies, Devon Hales Imdb, Equate Foaming Facial Cleanser Ingredients, How Are Light Cigarettes Made, Bowflex Selecttech 560 Stand,