K-Adapter

K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters

This repository is the official implementation of the paper "K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters", ACL-IJCNLP 2021 Findings.

In the K-adapter paper, we present a flexible approach that supports continual knowledge infusion into large pre-trained models (e.g. RoBERTa in this work). We infuse factual knowledge and linguistic knowledge, and show that adapters for both kinds of knowledge work well on downstream tasks.

For more details, please check the latest version of the paper: https://arxiv.org/abs/2002.01808

Prerequisites

  • Python 3.6
  • PyTorch 1.3.1
  • tensorboardX
  • transformers

We use huggingface/transformers framework, the environment can be installed with:

conda create -n kadapter python=3.6

pip install -r requirements.txt

Pre-training Adapters

In the pre-training procedure, we train each knowledge-specific adapter on different pre-training tasks individually.

1. Process Dataset

  • ./scripts/clean_T_REx.py: clean raw T-Rex dataset (32G), and save the cleaned T-Rex to JSON format
  • ./scripts/create_subdataset-relation-classification.ipynb: create the dataset from T-REx for pre-training factual adapter on relation classification task. This sub-dataset can be found here.
  • refer to this code to get the dependency parsing dataset : create the dataset from Book Corpus for pre-training the linguistic adapter on dependency parsing task.

2. Factual Adapter

To pre-train fac-adapter, run

bash run_pretrain_fac-adapter.sh

3. Linguistic Adapter

To pre-train lin-adapter, run

bash run_pretrain_lin-adapter.sh

The pre-trained fac-adapter and lin-adapter models can be found here.

Fine-tuning on Downstream Tasks

Adapter Structure

  • The fac-adapter (lin-adapter) consists of two transformer layers (L=2, H=768, A = 12)
  • The RoBERTa layers where adapters plug in: 0,11,23 or 0,11,22
  • For using only single adapter
    • Use the concatenation of the last hidden feature of RoBERTa and the last hidden feature of the adapter as the input representation for the task-specific layer.
  • For using combine adapter
    • For each adapter, first concat the last hidden feature of RoBERTa and the last hidden feature of every adapter and feed into a linear layer separately, then concat the representations as input for task-specific layer.

About how to load pretrained RoBERTa and pretrained adapter

  • The pre-trained adapters are in ./pretrained_models/fac-adapter/pytorch_model.bin and ./pretrained_models/lin-adapter/pytorch_model.bin.
    For using only single adapter, for example, fac-adapter, then you can set the argument meta_fac_adaptermodel=<the path of factual adapter model> and set meta_lin_adaptermodel=””.
    For using both adapters, just set the arguments meta_fac_adaptermodel and meta_lin_adaptermodel as the path of adapters.
  • The pretrained RoBERTa will be downloaded automaticly when you run the pipeline.

1. Entity Typing

1.1 OpenEntity

One single 16G P100

(1) run the pipeline

bash run_finetune_openentity_adapter.sh

(2) result

  • with fac-adapter
    dev: (0.7967123287671233, 0.7580813347236705, 0.7769169115682607)
    test: (0.7929708951125755, 0.7584033613445378, 0.7753020134228187)
  • with lin-adapter
    dev: (0.8071672354948806, 0.7398331595411888, 0.7720348204570185)
    test:(0.8001135718341851, 0.7400210084033614, 0.7688949522510232)
  • with fac-adapter + lin-adapter
    dev: (0.8001101321585903, 0.7575599582898853, 0.7782538832351366)
    test: (0.7899568034557235, 0.7627737226277372, 0.7761273209549072)

the results may vary when running on different machines, but should not differ too much.
I just search results from per_gpu_train_batch_sizeh: [4, 8] lr: [1e-5, 5e-6], warmup[0,200,500,1000,1200], maybe you can change other parameters and see the results.
For w/fac-adapter, the best performance is achieved at gpu_num=1, per_gpu_train_batch_size=4, lr=5e-6, warmup=500(it takes about 2 hours to get the best result running on singe 16G P100)
For w/lin-adapter, the best performance is achieved at gpu_num=1, per_gpu_train_batch_size=4, lr=5e-6, warmup=1000(it takes about 2 hours to get the best result running on singe 16G P100)

(3) Data format

Add special token "@" before and after a certain entity, then the first @ is adopted to perform classification.
9 entity categories: ['entity', 'location', 'time', 'organization', 'object', 'event', 'place', 'person', 'group'], each entity can be classified to several of them or none of them. The output is represented as [0,1,1,0,1,0,0,0,0], 0 represents the entity does not belong to the type, while 1 belongs to.

1.2 FIGER

(1) run the pipeline

bash run_finetune_figer_adapter.sh

The detailed hyperparamerters are listed in the running script.

2. Relation Classification

4*16G P100

(1) run the pipeline

bash run_finetune_tacred_adapter.sh

(2) result

  • with fac-adapter

    • 'dev': (0.6686945083853996, 0.7481604120676968, 0.7061989928807085)
    • 'test': (0.693900391717963, 0.7458646616541353, 0.7189447746050153)

  • with lin-adapter

    • 'dev': (0.6679165308118683, 0.7536791758646063, 0.7082108902333621),
    • 'test': (0.6884615384615385, 0.7536842105263157, 0.7195979899497488)

  • with fac-adapter + lin-adapter

    • 'dev': (0.6793893129770993, 0.7367549668874173, 0.7069102462271645)
    • 'test': (0.7014245014245014, 0.7404511278195489, 0.7204096561814192)

  • the results may vary when running on different machines, but should not differ too much.

  • I just search results from per_gpu_train_batch_sizeh: [4, 8] lr: [1e-5, 5e-6], warmup[0,200,1000,1200], maybe you can change other parameters and see the results.

  • The best performance is achieved at gpu_num=4, per_gpu_train_batch_size=8, lr=1e-5, warmup=200 (it takes about 7 hours to get the best result running on 4 16G P100)

  • The detailed hyperparamerters are listed in the running script.

(3) Data format

Add special token "@" before and after the first entity, add '#' before and after the second entity. Then the representations of @ and # are concatenated to perform relation classification.

3. Question Answering

3.1 CosmosQA

One single 16G P100

(1) run the pipeline

bash run_finetune_cosmosqa_adapter.sh

(2) result

CosmosQA dev accuracy: 80.9
CosmosQA test accuracy: 81.8

The best performance is achieved at gpu_num=1, per_gpu_train_batch_size=64, GRADIENT_ACC=32, lr=1e-5, warmup=0 (it takes about 8 hours to get the best result running on singe 16G P100)
The detailed hyperparamerters are listed in the running script.

(3) Data format

For each answer, the input is <s>context</s></s>question</s></s>answer</s>, and will get a score for this answers. After getting four scores, we will select the answer with the highest score.

3.2 SearchQA and Quasar-T

The source codes for fine-tuning on SearchQA and Quasar-T dataset are modified based on the code of paper "Denoising Distantly Supervised Open-Domain Question Answering".

Use K-Adapter just like RoBERTa

  • You can use K-Adapter (RoBERTa with adapters) just like RoBERTa, which almost have the same inputs and outputs. Specifically, we add a class RobertawithAdapter in pytorch_transformers/my_modeling_roberta.py.
  • A demo code [run_example.sh and examples/run_example.py] about how to use “RobertawithAdapter”, do inference, save model and load model. You can leave the arguments of adapters as default.
  • Now it is very easy to use Roberta with adapters. If you only want to use single adapter, for example, fac-adapter, then you can set the argument meta_fac_adaptermodel='./pretrained_models/fac-adapter/pytorch_model.bin'' and set meta_lin_adaptermodel=””. If you want to use both adapters, just set the arguments meta_fac_adaptermodel and meta_lin_adaptermodel as the path of adapters.
bash run_example.sh

TODO

  • Remove and merge redundant codes
  • Support other pre-trained models, such as BERT...

Contact

Feel free to contact Ruize Wang ([email protected]) if you have any further questions.

GitHub

https://github.com/microsoft/K-Adapter