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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

How to create your own Question and Answering API(Flask+Docker +BERT) using haystack framework

soubam Kohinoor
soubam Kohinoor Last Updated : 02 Feb, 2021
5 min read

Introduction

Note from the author: In this article, we will learn how to create your own Question and Answering(QA) API using python, flask, and haystack framework with docker. The haystack framework will provide the complete QA features which are highly scalable and customizable. In this article Medium Rules, the text will be used as the target document and fine-tuning the model as well.

Basic Knowledge Required: Elasticsearch & Docker

This article contains the working code which can be directly build using docker.

Content of the article

  1. DocumentStores set up — Installation of Elasticsearch
  2. API Code Explanation
  3. Build the Flask API in docker
  4. Load data from the API
  5. Demonstration of the API
  6. Fine-tuning a model
  7. Code tuning (Trained/Pre-Trained)
  8. Summarize Result

 

1. DocumentStores set up

Haystack raises queries to the document which is available at DocumentStore. There are various DocumentStore included in haystack which are ElasticsearchDocumentStore, SQLDocumentStore, and InMemoryDocumentStore. In this article, I am going to use Elasticsearch which is recommended. It comes up with preloaded features like full-text queries, BM25 retrieval, and vector storage for text embeddings.

Question and Answering API document store setup

Run the below command for the installation of Elasticsearch

Question and Answering API document store output

verify the installation by browsing the below URL. If install successfully it will display as in below:

Question and Answering API installing browser url

 

2. API Code Explanation

Haystack framework has main 3 basic components DocumentStore, Retriever, and Reader which we have to select based on our requirements.

DocumentStore: As recommended ElasticsearchDocumentStore will be used in this article. It comes preloaded with features like full-text queries, BM25 retrieval, and vector storage for text embeddings. Documents should be chunked into smaller units (e.g. paragraphs) before indexing to make the results returned by the Retriever more granular and accurate.

Retrievers: The answer needs to be display based on similarities on the embedded. So DensePassageRetriever is the powerful alternative to score the similarity of texts.

Readers: Farm Reader going to be used in this article if you wish Transformer readers can be used. For better accuracy “distilbert-base-uncased-distilled-squad” model going to be used by the readers.

The app structure as shown below:

API code explanation

app structure

Code Explanation for main.py

In this article, we are going to use the ElasticSearch document. The application configuration is declared as in below:

Let’s implement endpoints for uploading the PDF document. The pdf document will be uploaded in the ElasticSearch with the provided index name.

Let’s implement endpoints for querying and will respond back with relevant (n)answers from the ElasticSearch document. The index needs to provide during the search query.

requirement.txt

Flask
gunicorn
futures
farm-haystack

 

Docker File

The Question and Answering API will be available @port 8777.

 

3. Build the Flask API in docker

Let’s run the below command to build the docker image:

docker build -t qna:v1 .

Build and run the flask API in docker container using the below command:

docker run — name qna_app -d -p 8777:8777 xxxxxxxxx

Note: xxxxxxxxx is the image id

Confirm the docker container using the below command:

docker ps

Its will show all the process as in below:

Question and Answering API docker ps

Now the QNA API is successfully running @ http://localhost:8777

 

4. Load data from the API

Let’s prepare a pdf document from Medium Rules which suppose to be upload in an elastic search document. Once you prepare the pdf document lets to upload using the API as in the below snapshot:

Load data from the API

Verify the upload document as in the below snapshot:

snapshot

 

5. Demonstration of the API

Let’s ask a question using the qna_pretrain endpoints. Currently, we have used only the pre-train model “distilbert-base-uncased-distilled-squad” which is provided good accuracy. In the next article, I will demonstrate how to annotate and improve the model. For querying a question use the API as in the below snapshot:

Demonstration of the API

 

6. Fine-tuning a model

Let’s try to improve the model and train ourselves as per our requirements. For any domain-specific training we have to train ourselves, the Haystack Annotation tool will be used for labeling our data. In this article, I will use the hosted version of the annotation tool from the haystack.

For the local version follow here.https://github.com/deepset-ai/haystack/tree/master/annotation_tool

Now create an account with your personal email id and upload the txt document. Annotate your question and answers as in the below snapshot:

Fine-tuning a model

Now export all your labels in SQUAD format as “answers.json” and copy the file to the docker container with the below command:

Fine-tuning a model answer

Code explanation util-trainer.py

After the collection of the training labels, this python utility will help us to train and fine-tuning the model base on our requirements. Let’s proceed with the training with 10 epochs.

Use the below docker command to execute from the docker container:

Code explanation util-trainer.py

To start the training run the command “python util-trainer.py “

 

python util-trainer.py Question and Answering API

At the end of the training, the model will be saved in the train_model directory. Now training is successfully completed.

 

7. Code Tuning (Trained & Untrained) Model

Let’s modify the code to subscribed the trained and pre-trained model in the same endpoint. We are going to add the additional parameter “mode” to be classified the model.

Published the code to the docker contained with the below command:

docker copy main.py qna_app:/usr/src/app/main.py

Now restart the container to reflect the changes in the API

docker restart qna_app

Now the API is ready to subscribe with the trained & pre-trained model.

 

8. Summarize Result

Now, let’s compare the two models (trained and pre-trained) and analyze the most probability answer. Use the below API as in the snapshot:

 

Summarize Result Question and Answering API

the trained model gives the perfect results as we provided the label in training data.

I hope we have learned to develop our own Question and Answering API by fine-tuning the model.

The same article is published on my medium channel

The complete source code is available here

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion.

soubam Kohinoor
soubam Kohinoor

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Responses From Readers

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Sugumar
Sugumar

Very good and to the point article with coding examples. Thanks.

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