How to install and use spacy models?

How to install and use spacy models?

How to install and use spacy models?

This recipe helps you install and use spacy models

Recipe Objective

How we can install and use Spacy Models. Spacy is an open-source software library for advances natural language processing, and specifically designed for production use and helps to build applications that process understand large volumes of text. Also it can be used for information extraction.

Spacy Models These are the models which are used for tagging, parsing and entity recognition. Let us see how to install spacy models and how to use them.

Step 1 - Install Spacy using pip command

!pip install spacy

Step 2 - Download best matching version of specific model for our spacy installation

!python -m spacy download en_core_web_sm

Step 3 - Download best matching default model

!python -m spacy download en

Step 4 - Download exact model version

!python -m spacy download en_core_web_sm-2.2.0

Step 5 - Import Spacy and load Model

import spacy load_model = spacy.load("en_core_web_sm") doc = load_model("Hi my name is mak") doc

Hi my name is mak

Relevant Projects

Image Segmentation using Mask R-CNN with Tensorflow
In this Deep Learning Project on Image Segmentation Python, you will learn how to implement the Mask R-CNN model for early fire detection.

Inventory Demand Forecasting using Machine Learning in R
In this machine learning project, you will develop a machine learning model to accurately forecast inventory demand based on historical sales data.

German Credit Dataset Analysis to Classify Loan Applications
In this data science project, you will work with German credit dataset using classification techniques like Decision Tree, Neural Networks etc to classify loan applications using R.

Machine Learning or Predictive Models in IoT - Energy Prediction Use Case
In this machine learning and IoT project, we are going to test out the experimental data using various predictive models and train the models and break the energy usage.

Predict Churn for a Telecom company using Logistic Regression
Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.

Predict Employee Computer Access Needs in Python
Data Science Project in Python- Given his or her job role, predict employee access needs using amazon employee database.

Demand prediction of driver availability using multistep time series analysis
In this supervised learning machine learning project, you will predict the availability of a driver in a specific area by using multi step time series analysis.

Build a Music Recommendation Algorithm using KKBox's Dataset
Music Recommendation Project using Machine Learning - Use the KKBox dataset to predict the chances of a user listening to a song again after their very first noticeable listening event.

PySpark Tutorial - Learn to use Apache Spark with Python
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.

Abstractive Text Summarization using Transformers-BART Model
Deep Learning Project to implement an Abstractive Text Summarizer using Google's Transformers-BART Model to generate news article headlines.