few-shot learning
Each folder contains an implementation of each model.
- data_helper. data processing
- model. model construction
- trainer. train model
- metrics. performance metrics
- config.json Configuration files for model parameters and training parameters
induction_network
- paper: Few-Shot Text Classification with Induction Network
relation_network
- paper: Learning to Compare: Relation Network for Few-Shot Learning
prototypical_network
- paper: Prototypical Networks for Few-shot Learning
siamese_network
- paper: Siamese Neural Networks for One-shot Image Recognition
ARSC data set
- the data from Amazon Review Data Set, arranged by Alibaba Group
- citation: Image-based recommendations on styles and substitutes J. McAuley, C. Targett, J. Shi, A. van den Hengel SIGIR, 2015
- citation: Mo Yu, Xiaoxiao Guo, Jinfeng Yi, Shiyu Chang, Saloni Potdar, Yu Cheng, Gerald Tesauro, Haoyu Wang, and Bowen Zhou. 2018. Diverse few-shot text classification with multiple metrics
word vector
- using glove word vector, you need download 300 dim glove word vector and place it in word_embedded dir.
note
- You can only use 2-way, and if you need to use other way, you can modify the data_helper.py file.
- Shot should not be more than 10, because there are few comments under some categories.
- The number of categories in prediction and training can not be equal.