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summary-reward-no-reference

A reference-free metric for measuring summary quality, learned from human ratings.

sumsjob

A simple Linux command-line utility which submits a job to one of the multiple GPU servers

supervised_classification_app

Supervised Classification learning techniques are useful in designing mobile apps as they entail guided examples capturing specific human needs and their.

support-ctx-bot

t5-finetuning-pytorch

Fine tune a T5 transformer model using PyTorch & Transformers🤗

t5-text-to-text-transfer-transformer

Demo of the T5 model for various pre-trained task.

t5_multitask_api

t5_nlg

Code for the medium blogpost "Data to Text generation with T5; Building a simple yet advanced NLG model"

tanl

Structured Prediction as Translation between Augmented Natural Languages

task_oriented_dialogue_as_dataflow_synthesis

Code to reproduce experiments in the paper "Task-Oriented Dialogue as Dataflow Synthesis" (TACL 2020).

tensorflow-101

TensorFlow 101: Introduction to Deep Learning

tensorflow-developer-practise

These are my Notebooks while I am training and practicing to be fluent in tensorflow 2.0 to clear the Tensorflow developer certification

testimages

Test images for graph-based image segmentation.

text

Making text a first-class citizen in TensorFlow.

text-classifier-texthero-fasttext

text-generation-using-bidirectional-lstm-and-doc2vec-models

Text Generation using Bidirectional LSTM and Doc2Vec models

text-to-text-transfer-transformer

Code for the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer"

text2text

Text2Text: Crosslingual NLP/G toolkit

textprediction

Recent Google and Facebook focused on behind-the-scenes mechanisms of text prediction. In addition to using Recurrent Neural Network and Long Short-Term Memory Networks for the motivation, there were two word2vec models for generating word embeddings also discussed.

tf_deep_decoder

Reimplementation of the Deep Decoder architecture in Tensorflow

time_series_forecasting_r

Time series forecasting occurs when you make scientific predictions based on historical time stamped data. It involves building models through historical analysis and using them to make observations and drive future strategic decision-making.

transfer-learning-for-nlp

Companion repository to Paul Azunre's "Transfer Learning for Natural Language Processing" book

transformers

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

transformers-1

tree-bark-classification

turkish-question-generation

Automated question generation and question answering from Turkish texts using text-to-text transformers

tutorial-machine-learning

tutorials

PyTorch tutorials.

tutorials-1

Tutorials on deep learning, Python, and dissipative particle dynamics

udemy_nlu