readme

README.md

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 # Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification
-Code and data of AAAI2019 paper _Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification_.
+
+Code and data for AAAI2019 paper _Hybrid Attention-Based Prototypical Networks for Noisy Few-Shot Relation Classification_.
+
+Author: Tianyu Gao*, Xu Han*, Zhiyuan Liu, Maosong Sun. (\* means equal contribution)
+
+## Dataset and Word Embedding
+
+We evaluate our models on [FewRel](https://thunlp.github.io/fewrel), a large-scale dataset for few-shot relation classification. It has 100 relations and 700 instances for each relation. You can find some baseline models from [here](https://github.com/thunlp/fewrel).
+
+Due to the large size, we did not upload the glove file (pre-trained word embedding). Please download `glove.6B.50d.json` from [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/f/b14bf0d3c9e04ead9c0a/?dl=1) or [Google Drive](https://drive.google.com/open?id=1UnncRYzDpezPkwIqhgkVW6BacIqz6EaB) and put it under `data/` folder.
+
+## Usage
+
+To run our code, use this command for training
+```bash
+python train.py {MODEL_NAME} {N} {K} {NOISE_RATE}
+```
+and use this command for testing
+```bash
+python test.py {MODEL_NAME} {N} {K} {NOISE_RATE}
+```
+where {MODEL_NAME} could be `proto` or `proto_hatt`, `{N}` is the num of classes, `{K}` is the num of instances for each class and `{NOISE_RATE}` is the probability that one instance is wrong-labeled.