Abhiramias09's Stars
zyydoosh/RankResumes
Model to rank resumes based on job description using NLP and machine learning techniques
zhangzibin/PairCNN-Ranking
A tensorflow implementation of Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks
sachinraghult/Resume-Ranker
A job finder kinda application with option of jobs hiring giving scope to our main objective of Resume Ranking involving Machine Learning with MERN stack support that uses Celery and RabbitMQ as message queues and Docker as a container to automatically rank resumes using NLP and NER model
arpit3043/Extractive-Text-Summerization
Summarization systems often have additional evidence they can utilize in order to specify the most important topics of document(s). For example, when summarizing blogs, there are discussions or comments coming after the blog post that are good sources of information to determine which parts of the blog are critical and interesting. In scientific paper summarization, there is a considerable amount of information such as cited papers and conference information which can be leveraged to identify important sentences in the original paper. How text summarization works In general there are two types of summarization, abstractive and extractive summarization. Abstractive Summarization: Abstractive methods select words based on semantic understanding, even those words did not appear in the source documents. It aims at producing important material in a new way. They interpret and examine the text using advanced natural language techniques in order to generate a new shorter text that conveys the most critical information from the original text. It can be correlated to the way human reads a text article or blog post and then summarizes in their own word. Input document → understand context → semantics → create own summary. 2. Extractive Summarization: Extractive methods attempt to summarize articles by selecting a subset of words that retain the most important points. This approach weights the important part of sentences and uses the same to form the summary. Different algorithm and techniques are used to define weights for the sentences and further rank them based on importance and similarity among each other. Input document → sentences similarity → weight sentences → select sentences with higher rank. The limited study is available for abstractive summarization as it requires a deeper understanding of the text as compared to the extractive approach. Purely extractive summaries often times give better results compared to automatic abstractive summaries. This is because of the fact that abstractive summarization methods cope with problems such as semantic representation, inference and natural language generation which is relatively harder than data-driven approaches such as sentence extraction. There are many techniques available to generate extractive summarization. To keep it simple, I will be using an unsupervised learning approach to find the sentences similarity and rank them. One benefit of this will be, you don’t need to train and build a model prior start using it for your project. It’s good to understand Cosine similarity to make the best use of code you are going to see. Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them. Since we will be representing our sentences as the bunch of vectors, we can use it to find the similarity among sentences. Its measures cosine of the angle between vectors. Angle will be 0 if sentences are similar. All good till now..? Hope so :) Next, Below is our code flow to generate summarize text:- Input article → split into sentences → remove stop words → build a similarity matrix → generate rank based on matrix → pick top N sentences for summary.
SiddharthSelvaraj/Learning-To-Rank-Using-Linear-Regression-and-Stochastic-Gradient-Descent
The goal of the problem is to solve the Learning to Rank (LeToR) problem using Linear Regression. For the given LeToR and synthetic datasets, linear regression models are trained using closed-form solution and stochastic gradient descent (SGD). The given datasets are partitioned into training set (80%), validation set (10%) and testing set (10%) such that they do not overlap. For the hyper-parameters such as M, µj , Σj , λ, η (τ) , the model parameter w on the training set is trained from the closed-form solution and stochastic gradient descent (SGD). The regression model is validated on the validation set and the hyper-parameters are changed to give a better performance on the validation set. Then the performance of the model on the testing set is tested by fixing the hyperparameters and model parameters. This shows the ultimate effectiveness of the model’s generalization power gained by learning.
jkaub/toxicity-ranker
This repo introduce a notebook to train a deep learning model with PyTorch and Hugging Face in order to rank messages by toxicity
MandyZzZz/Learn-to-Rank
A ranking model that utilized supervised machine learning to recommend the most relevant product to users
marcmento/Learning-to-Rank
Learning to Rank machine learning model
Datatouille/sushirank
Educational implementation of pointwise and pairwise learning-to-rank models
o19s/elasticsearch-ltr-demo
This demo uses data from TheMovieDB (TMDB) to demonstrate using Ranklib learning to rank models with Elasticsearch.
allegro/allRank
allRank is a framework for training learning-to-rank neural models based on PyTorch.
shiba24/learning2rank
Learning to rank with neuralnet - RankNet and ListNet
andreweskeclarke/learning-rank-public
Learning Rank
jdorri/BART-news-summaries
News summarization app using Hugging Face Transformers, spaCy, and Streamlit
debamitr1012/News-Summarization-App
BART news summarization