rishabbh-sahu Goto Github PK

explicable_ai

To understand how variety of ML models work and their interpretation

extractive_summarization

Extractive summarization based on significant sentence present in the article. Significant sentences can be found based on word frequency count. Spacy library used for text pre-processing.

ignore_email_address_classifier

Important vs Ignore email classifier based on incoming email addresses. Bert tokenizer is used as a tokenization method and CNN network as the model. Framework used - Tensorflow 2.4

information_retrieval

Given a document, identifying the closest documents within the list of documents using tf-idf matrix and cosine similarity

intent_and_slot_classification

One of the main NLU tasks is to understand the intents (sequence classification) and slots (entities within the sequence). This repo help classify both together using Joint Model (multitask model). BERT_SMALL is used which can be changed to any other BERT variant.

learning_pathways

A repository consists of various links which helps understanding a concepts, algorithm etc.

linear_programming

Solving linear programming using python optimizer interface. This repo allows you to add multiple decision vars and constraints etc. in a very easy way.

rishabbh-sahu

semantic_lookalike_transformers

Finding look alike sentences by leveraging the concept of semantic similarities pre-learned by transformer models while pre-training task. I've used cosine similarity as an angular distance matrix applied over sent2vec.

text_preprocessing_docker_implementation

Simple approach to fetch certain info from the text, provided to it and integration with the docker.