Prithivida (PrithivirajDamodaran)

PrithivirajDamodaran

Geek Repo

Location:India

Home Page:https://www.youtube.com/@prithivida

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Prithivida's repositories

Gramformer

A framework for detecting, highlighting and correcting grammatical errors on natural language text. Created by Prithiviraj Damodaran. Open to pull requests and other forms of collaboration.

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Parrot_Paraphraser

A practical and feature-rich paraphrasing framework to augment human intents in text form to build robust NLU models for conversational engines. Created by Prithiviraj Damodaran. Open to pull requests and other forms of collaboration.

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Styleformer

A Neural Language Style Transfer framework to transfer natural language text smoothly between fine-grained language styles like formal/casual, active/passive, and many more. Created by Prithiviraj Damodaran. Open to pull requests and other forms of collaboration.

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FlashRank

Lite & Super-fast re-ranking for your search & retrieval pipelines. Supports SoTA Listwise and Pairwise reranking based on LLMs and cross-encoders and more. Created by Prithivi Da, open for PRs & Collaborations.

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ZSIC

Zero Shot Image Classification but more, Supports Multilingual labelling and a variety of CNN based models for a vision backbone by using OpenAI CLIP for $ conscious uses (Super simple, so a 10th-grader could totally write this but since no 10th-grader did, I did) - Prithivi Da

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Alt-ZSC

Alternate Implementation for Zero Shot Text Classification: Instead of reframing NLI/XNLI, this reframes the text backbone of CLIP models to do ZSC. Hence, can be lightweight + supports more languages without trading-off accuracy. (Super simple, a 10th-grader could totally write this but since no 10th-grader did, I did) - Prithivi Da

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SPLADERunner

Lite weight wrapper for the independent implementation of SPLADE++ models for search & retrieval pipelines. Models and Library created by Prithivi Da, For PRs and Collaboration checkout the readme.

blitz-embed

C++ inference wrappers for running blazing fast embedding services on your favourite serverless like AWS Lambda. By Prithivi Da, PRs welcome.

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vision-language-modelling-series

Companion Repo for the Vision Language Modelling YouTube series - https://bit.ly/3PsbsC2 - by Prithivi Da. Open to PRs and collaborations

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Applied-ML-from-1st-principles

Companion Repo for the book The Applied ML Field Manual, Prithiviraj Damodaran

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langid-benchmark

A benchmark of off-the-shelf models that detects language in text - Contributions welcome

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VisionLanguageProgress

Repository to track the progress in V+L Multimodal Learning, including the datasets and the state-of-the-art of the current pre-trained models on various V+L tasks

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bias-bench

Extension of bias-bench for select Sentence Transformers* models

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Condenser

EMNLP 2021 - Pre-training architectures for dense retrieval

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llama.cpp

LLM inference in C/C++

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pyserini

Pyserini is a Python toolkit for reproducible information retrieval research with sparse and dense representations.

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sentence-transformers

Multilingual Sentence & Image Embeddings with BERT

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transformers

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

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beir

A Heterogeneous Benchmark for Information Retrieval. Easy to use, evaluate your models across 15+ diverse IR datasets.

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example-app-editable-dataframe

This is a demo of a dataframe with editable cells, powered by `streamlit-aggrid` from Pablo Fonseca. You can edit the cells by clicking on them and then export your selection to a csv file! 🎈

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Face-Transformer

Face Transformer for Recognition

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InsightFace_Pytorch

Pytorch0.4.1 codes for InsightFace

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js-snackbar

A simple, jQuery free Snackbar (Toast) alert

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llama-cpp-python

Python bindings for llama.cpp

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mlm-scoring

Extension of MLM-scoring Sentence Transformers models which are debiased

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neural-cherche

Neural Search

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pytorch-insightface

Pretrained insightface models ported to pytorch

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stable_diffusion_playground

Playing around with stable diffusion. Generated images are reproducible because I save the metadata and latent information. You can generate and then later interpolate between the images of your choice.

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VQA-Med-2019

Visual Question Answering in the Medical Domain VQA-Med 2019

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