This repo will contain various llm models whihc has been tested out and are specific to a given document rather than simple text generation.
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Updated
Sep 25, 2023 - Jupyter Notebook
This repo will contain various llm models whihc has been tested out and are specific to a given document rather than simple text generation.
My implementation of various popular transformer architectures
Deep Learning based Machine translation Models
A repository to finetune transformers on a multilabel classification task ( based on transformers library ).
NLP Service to perform text classification. This is the first part of Project Jarvis. This service integrates to the chat-bot service
Implementation of the semi-structured inference model in our ACL 2020 paper. INFOTABS: Inference on Tables as Semi-structured Data
Feature engineering with the help of HuggingFace transformers, Tensorflow, Keras, TextBlob, NLTK, Sci-kit learn etc.
Basic Sentiment Analysis with transformers library and streamlit
An experimental framework for evaluating the JuggleChat multiagent system.
Quickly generate positional embeddings using an ultra small transformer models for maximum speed
A set of multi-label classification models which are capable of detecting different types of toxicity: toxic, severe_toxic, obscene, threat, insults and identity_hate .
Syllabification of verses in hendecasyllable respecting metric figures
A chrome extension that can summarize the transcript of youtube videos.
Engineered an innovative PDF Reader Question-Answering Bot that synergized Falcon's question comprehension, Chainlilt's intuitive UI, and Transformers from Hugging Face for contextual responses. This pioneering approach seamlessly addresses user queries within PDFs, significantly enhancing accessibility and understanding.
Sediment analysis of movie review with using differnt approaches (Spacy, NLTK, BERT)
A Zeebe named-entity recognition (NER) worker based on Hugging Face NLP pipeline
Benchmark training and inference time for Transformer models on Huggingface
Summarizing_given_text_using_T5(Transformers).
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