Pinned Repositories
BiasInBerts
An analysis of the stereotypical bias present in BERT language models pretrained on scientific corpora
binaryclassification
Using magic dataset to test supervised binary classification models
CS_EcoSimulator
CounterStrike : Global Offensive Economy Simulator in Python. My personal project.
FakeReviewDetection
Project for DS-GA 1003 Machine Learning. Identifying Fake Reviews using Yelp training data. Scoring and submission through Codalab
grandstanding
LegalUISRNN
This is the library for the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm, corresponding to the paper Fully Supervised Speaker Diarization.
rd-diarization
Diarizing Legal Proceedings with d-vectors.
SciStereo
StereoSet: Measuring stereotypical bias in pretrained language models
SpeakerVerificationEmbedding
PyTorch implementation of "Generalized End-to-End Loss for Speaker Verification" by Wan, Li et al.
VoiceEncoder
A python package to analyze and compare voices with deep learning
JeffT13's Repositories
JeffT13/rd-diarization
Diarizing Legal Proceedings with d-vectors.
JeffT13/CS_EcoSimulator
CounterStrike : Global Offensive Economy Simulator in Python. My personal project.
JeffT13/FakeReviewDetection
Project for DS-GA 1003 Machine Learning. Identifying Fake Reviews using Yelp training data. Scoring and submission through Codalab
JeffT13/VoiceEncoder
A python package to analyze and compare voices with deep learning
JeffT13/BiasInBerts
An analysis of the stereotypical bias present in BERT language models pretrained on scientific corpora
JeffT13/binaryclassification
Using magic dataset to test supervised binary classification models
JeffT13/grandstanding
JeffT13/LegalUISRNN
This is the library for the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm, corresponding to the paper Fully Supervised Speaker Diarization.
JeffT13/SciStereo
StereoSet: Measuring stereotypical bias in pretrained language models
JeffT13/SpeakerVerificationEmbedding
PyTorch implementation of "Generalized End-to-End Loss for Speaker Verification" by Wan, Li et al.