Natural Language Processing Best Practices & Examples
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Updated
Aug 30, 2022 - Python
Natural Language Processing Best Practices & Examples
A private nlp coding package, which quickly implements the SOTA solutions.
This repository contains a DistilBERT model fine-tuned using the Hugging Face Transformers library on the IMDb movie review dataset. The model is trained for sentiment analysis, enabling the determination of sentiment polarity (positive or negative) within text reviews.
Official implementation of the paper "Deep Learning for Hate Speech Detection -A Comparative Study"
基于pytorch框架,针对文本分类的机器学习项目,集成多种算法(xgboost, lstm, bert, mezha等等),提供基础数据集,开箱即用,方便自己二次拓展,持续更新
Implementations of transformer models in pytorch
An end to end ASR Transformer model training repo
A TypeScript custom transformer to obtain JSON schema from interface.
PlexusNet for medical imaging
This repository contains the code of the experiments reported in the paper "Syntax-aware Transformers for Neural Machine Translation: The Case ofText to Sign Gloss Translation". This paper was accepted for the Workshop BUCC2021 in the conference RANLP 2021.
Future power consumption prediction using LSTM, GRU and Transformer models
Implementing Transfomer by PyTorch (in Colab)
This project is about end-to-end implementation of sentence paraphrasing model using the docker
This is a Python package for accelerating the inference of Large Language Models (LLMs) by Speculative Decoding (SD), especially for Beam Search.
Text translation and sentiment analysis using transformers using PyTorch and Jupyter Notebook.
Annotated vanilla implementation in PyTorch of the Transformer model introduced in 'Attention Is All You Need'.
A Novel Abstractive Text Summarization approach for Lecture Content
Fine-tuned BERT on down-stream task as Multi Label classfication.
Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding - Tensorflow
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