Xiaorui Jiang's repositories
DeepLearningForNLPInPytorch
An IPython Notebook tutorial on deep learning for natural language processing, including structure prediction.
bert-crf
Solutions of the problems NER and RE in the domain of business documents with the BERT+CRF model.
dataHacker
code for dataHacker blog
AMuLaP_Automatic-Multi-Label-Prompting__NAACL2022
Code for NAACL 2022 paper "Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification"
Medical-NER__Good_for_Learning
Notebook for BERT medical named entity recognition
xiaoruijiang.github.io
Xiaorui Jiang's Academic Home Page
transformers-tasks-templates
Collection of templates for different tasks using 🤗 Transformers
master-thesis-proposals
This repo contains master thesis proposals available at DBMDG Group (Politecnico di Torino) for Deep Learning applied to NLP and Audio Processing.
WZU-machine-learning-course
温州大学《机器学习》课程资料（代码、课件等）
nlp-recipes__microsoft_at_GitHub
Natural Language Processing Best Practices & Examples
LM-BFF__ACL2021
[ACL 2021] LM-BFF: Better Few-shot Fine-tuning of Language Models https://arxiv.org/abs/2012.15723
SUMMER__Screenplay-Summarization-Using-Latent-Narrative-Structure
Screenplay Summarization using Latent Narrative Structure
LAAT
A Label Attention Model for ICD Coding from Clinical Text
nativeinformation
Towards employing native information in citation function classifcation
JBI2020-Explainable-Automated-Medical-Coding
Implementation and demo of explainable coding of clinical notes with Hierarchical Label-wise Attention Networks (HLAN)
THExt
THExt - Transformer-based Highlights Extraction
covid19-law-matching__LREC2022
Claim retrieval and matching with laws for COVID-19 related legislation (LREC 2022).
Reinforcement-Learning-Maze
Various ways to learn a computer to escape from a maze. From random walk to a simple neural network.
Coursera-ML-AndrewNg-Notes
吴恩达老师的机器学习课程个人笔记
Channel-LM-Prompting__ACL2022
An original implementation of "Noisy Channel Language Model Prompting for Few-Shot Text Classification"
PERFECT_ACL2022
PERFECT: Prompt-free and Efficient Few-shot Learning with Language Models