Education: Starter Kit - Student Counsellor

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by kandikits dot icon Updated: Jan 24, 2022

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Solution Kit Solution Kit Β 

Student Counsellor can help the student across many areas such as: 1. Clarifications on subjects of your choice. 2. Help students to prepare for admissions applications and tests. 3. Provide one-on-one career guidance and skills assessment to assist with career development 4. Offer referrals to outside resources, such as for mental health, substance abuse, or vocational-related activities. In this challenge, we are inviting to build a virtual counsellor application which will help with addressing student queries round the clock. You can also choose any topics of your choice. Please see below a sample solution kit to jumpstart your solution on creating a Virtual Agent application. To use this kit to build your own solution, scroll down to refer sections Kit Deployment Instructions and Instruction to Run. Complexity : Simple

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

jupyterby jupyter

Python doticonstar image 14404 doticonVersion:Currentdoticon
License: Permissive (BSD-3-Clause)

Jupyter metapackage for installation, docs and chat

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            jupyterby jupyter

            Python doticon star image 14404 doticonVersion:Currentdoticon License: Permissive (BSD-3-Clause)

            Jupyter metapackage for installation, docs and chat
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                      vscodeby microsoft

                      TypeScript doticonstar image 147328 doticonVersion:1.79.2doticon
                      License: Permissive (MIT)

                      Visual Studio Code

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                                vscodeby microsoft

                                TypeScript doticon star image 147328 doticonVersion:1.79.2doticon License: Permissive (MIT)

                                Visual Studio Code
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                                          Exploratory Data Analysis

                                          For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation. For building Virtual Agent, we use pandas to load, view and analyse data from csv file; and we use numpy to find out the indices of maximum values of an array.

                                          pandasby pandas-dev

                                          Python doticonstar image 38689 doticonVersion:v2.0.2doticon
                                          License: Permissive (BSD-3-Clause)

                                          Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

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                                                    pandasby pandas-dev

                                                    Python doticon star image 38689 doticonVersion:v2.0.2doticon License: Permissive (BSD-3-Clause)

                                                    Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
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                                                              numpyby numpy

                                                              Python doticonstar image 23755 doticonVersion:v1.25.0rc1doticon
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                                                              The fundamental package for scientific computing with Python.

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                                                                        numpyby numpy

                                                                        Python doticon star image 23755 doticonVersion:v1.25.0rc1doticon License: Permissive (BSD-3-Clause)

                                                                        The fundamental package for scientific computing with Python.
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                                                                                  Text Mining

                                                                                  Libraries in this group are used for analysis and processing of unstructured natural language. The data, as in its original form aren't used as it has to go through processing pipeline to become suitable for applying machine learning techniques and algorithms. We use py-lingualytics to manage pre-processing of data like removing numerical values, stopwords, punctuations and so on.

                                                                                  py-lingualyticsby lingualytics

                                                                                  Python doticonstar image 32 doticonVersion:Currentdoticon
                                                                                  License: Permissive (MIT)

                                                                                  A text analytics library with support for codemixed data

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                                                                                            py-lingualyticsby lingualytics

                                                                                            Python doticon star image 32 doticonVersion:Currentdoticon License: Permissive (MIT)

                                                                                            A text analytics library with support for codemixed data
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                                                                                                      spaCyby explosion

                                                                                                      Python doticonstar image 26383 doticonVersion:v3.2.6doticon
                                                                                                      License: Permissive (MIT)

                                                                                                      πŸ’« Industrial-strength Natural Language Processing (NLP) in Python

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                                                                                                                spaCyby explosion

                                                                                                                Python doticon star image 26383 doticonVersion:v3.2.6doticon License: Permissive (MIT)

                                                                                                                πŸ’« Industrial-strength Natural Language Processing (NLP) in Python
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                                                                                                                          Machine Learning

                                                                                                                          Machine learning libraries and frameworks here are helpful in capturing state-of-the-art embeddings. Embeddings are vectoral representation of text with their semantics. For generating sentence embedding, we use sentence-transformers with pretrained models. We reference bert-cosine-sim to build similarity engine for comparing two sets of input text.

                                                                                                                          Python doticonstar image 10938 doticonVersion:v2.2.2doticon
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                                                                                                                          Multilingual Sentence & Image Embeddings with BERT

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                                                                                                                                    sentence-transformersby UKPLab

                                                                                                                                    Python doticon star image 10938 doticonVersion:v2.2.2doticon License: Permissive (Apache-2.0)

                                                                                                                                    Multilingual Sentence & Image Embeddings with BERT
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                                                                                                                                              bert-cosine-simby beekbin

                                                                                                                                              Python doticonstar image 61 doticonVersion:Currentdoticon
                                                                                                                                              no licences License: No License (null)

                                                                                                                                              Fine-tune BERT to generate sentence embedding for cosine similarity

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                                                                                                                                                        bert-cosine-simby beekbin

                                                                                                                                                        Python doticon star image 61 doticonVersion:Currentdoticonno licences License: No License

                                                                                                                                                        Fine-tune BERT to generate sentence embedding for cosine similarity
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                                                                                                                                                                  faissby facebookresearch

                                                                                                                                                                  C++ doticonstar image 22571 doticonVersion:v1.7.4doticon
                                                                                                                                                                  License: Permissive (MIT)

                                                                                                                                                                  A library for efficient similarity search and clustering of dense vectors.

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                                                                                                                                                                            faissby facebookresearch

                                                                                                                                                                            C++ doticon star image 22571 doticonVersion:v1.7.4doticon License: Permissive (MIT)

                                                                                                                                                                            A library for efficient similarity search and clustering of dense vectors.
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                                                                                                                                                                                      Kit Solution Source

                                                                                                                                                                                      faq-virtual-agentby kandikits

                                                                                                                                                                                      Jupyter Notebook doticonstar image 0 doticonVersion:Currentdoticon
                                                                                                                                                                                      License: Permissive (Apache-2.0)

                                                                                                                                                                                      Build your FAQ virtual agent in 5 minutes

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                                                                                                                                                                                                faq-virtual-agentby kandikits

                                                                                                                                                                                                Jupyter Notebook doticon star image 0 doticonVersion:Currentdoticon License: Permissive (Apache-2.0)

                                                                                                                                                                                                Build your FAQ virtual agent in 5 minutes
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                                                                                                                                                                                                          Deployment Information

                                                                                                                                                                                                          Virtual Counsellor created using this kit are added in this section. The entire solution is available as a package to download from the source code repository.

                                                                                                                                                                                                          For Windows OS, Download, extract and double-click kit_installer file to install the kit. Note: Do ensure to extract the zip file before running it. The installation may take from 2 to 10 minutes based on bandwidth. 1. When you're prompted during the installation of the kit, press Y to launch the app automatically and execute cells in the notebook by selecting Cell --> Run All from Menu bar to see how the chatbot works. It is loaded with minimal questions. 2. To run the app manually, press N when you're prompted and locate the zip file faq-virtual-agent.zip 3. Extract the zip file and navigate to the directory faq-virtual-agent-main 4. Open command prompt in the extracted directory faq-virtual-agent-main and run the command jupyter notebook For other Operating System, 1. Click here to install python 2. Click here to download the repository 3. Extract the zip file and navigate to the directory faq-virtual-agent-main 4. Open terminal in the extracted directory faq-virtual-agent-main 5. Install dependencies by executing the command pip install -r requirements.txt 6. Run the command jupyter notebook

                                                                                                                                                                                                          Instruction to Run

                                                                                                                                                                                                          Follow below instructions to run the solution. 1. Locate and open the Student Counsellor App.ipynb notebook from the Jupyter Notebook browser window. 2. Execute cells in the notebook by selecting Cell --> Run All from Menu bar For customizing to your Questions and Answers, 1. Open the input file faqs.csv in the faq-virtual-agent-main directory from the kit_installer.bat location 2. Update questions for training under the column Q and update corresponding answer in the column A 3. Execute cells in the notebook by selecting Cell --> Run All from Menu bar 4. Type in the query when you're prompted and hit Enter key Input file: faqs.csv - contains questions and answers to train You can additionally build interfaces to the chatbot and other enhancements for additional score. For any support, you can direct message us at #help-with-kandi-kits

                                                                                                                                                                                                          Troubleshooting

                                                                                                                                                                                                          1. While running batch file, if you encounter Windows protection alert, select More info --> Run anyway 2. During kit installer, if you encounter Windows security alert, click Allow

                                                                                                                                                                                                          Support

                                                                                                                                                                                                          If you need help to use this kit, you can email us at kandi.support@openweaver.com or direct message us on Twitter Message @OpenWeaverInc.