https://github.com/google-research/adapter-bert

https://github.com/google-research/adapter-bert

Last synced: 3 months ago
JSON representation

• Host: GitHub
• URL: https://github.com/google-research/adapter-bert
• Owner: google-research
• License: apache-2.0
• Created: 2019-05-21T14:27:47.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2023-10-25T12:26:21.000Z (8 months ago)
• Last Synced: 2024-01-22T22:49:42.649Z (5 months ago)
• Language: Python
• Size: 54.7 KB
• Stars: 446
• Watchers: 10
• Forks: 46
• Open Issues: 7
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome-stars - google-research/adapter-bert - (Python)

README

        
# Adapter-BERT

## Introduction

This repository contains a version of BERT that can be trained using adapters.

Our ICML 2019 paper contains a full description of this technique:

[Parameter-Efficient Transfer Learning for NLP](http://proceedings.mlr.press/v97/houlsby19a.html).

Adapters allow one to train a model to solve new tasks, but adjust only a few

parameters per task. This technique yields compact models that share many

parameters across tasks, whilst performing similarly to fine-tuning the entire

model independently for every task.

The code here is forked from the

[original BERT repo](https://github.com/google-research/bert).

It provides our version of BERT with adapters, and the capability to train it on

the [GLUE tasks](https://gluebenchmark.com/).

For additional details on BERT, and support for additional tasks, see the

original repo.

## Tuning BERT with Adapters

The following command provides an example of tuning with adapters on GLUE.

Fine-tuning may be run on a GPU with at least 12GB of RAM, or a Cloud TPU. The

same constraints apply as for full fine-tuning of BERT. For additional details,

and instructions on downloading a pre-trained checkpoint and the GLUE tasks,

see

[https://github.com/google-research/bert](https://github.com/google-research/bert).

```shell

export BERT_BASE_DIR=/path/to/bert/uncased_L-12_H-768_A-12

export GLUE_DIR=/path/to/glue

python run_classifier.py \

  --task_name=MRPC \

  --do_train=true \

  --do_eval=true \

  --data_dir=$GLUE_DIR/MRPC \

  --vocab_file=$BERT_BASE_DIR/vocab.txt \

  --bert_config_file=$BERT_BASE_DIR/bert_config.json \

  --init_checkpoint=$BERT_BASE_DIR/bert_model.ckpt \

  --max_seq_length=128 \

  --train_batch_size=32 \

  --learning_rate=3e-4 \

  --num_train_epochs=5.0 \

  --output_dir=/tmp/adapter_bert_mrpc/

```

You should see an output like this:

```

***** Eval results *****

  eval_accuracy = 0.85784316

  eval_loss = 0.48347527

  global_step = 573

  loss = 0.48347527

```

This means that the Dev set accuracy was 85.78%. Small sets like MRPC have a

high variance in the Dev set accuracy, even when starting from the same

pre-training checkpoint. Therefore results may deviate from this by 2%.

## Citation

Please use the following citation for this work:

```

@inproceedings{houlsby2019parameter,

  title = {Parameter-Efficient Transfer Learning for {NLP}},

  author = {Houlsby, Neil and Giurgiu, Andrei and Jastrzebski, Stanislaw and Morrone, Bruna and De Laroussilhe, Quentin and Gesmundo, Andrea and Attariyan, Mona and Gelly, Sylvain},

  booktitle = {Proceedings of the 36th International Conference on Machine Learning},

  year = {2019},

}

```

The paper is uploaded to [ArXiv](https://arxiv.org/abs/1902.00751).

## Disclaimer

This is not an official Google product.

## Contact information

For personal communication, please contact Neil Houlsby

([email protected]).