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README.md

BERT on STILTs

STILTs = Supplementary Training on Intermediate Labeled-data Tasks

This repository contains code for BERT on STILTs. It is a fork of the Hugging Face implementation of BERT.

STILTs is a method for supplementary training on an intermediate task before fine-tuning for a downstream target task. We show in our paper that STILTs can improve the performance and stability of the final model on the target task.

BERT on STILTs achieves a GLUE score of 82.0, compared to 80.5 of BERT without STILTs.

Trained Models

Base Model Intermediate Task Target Task Download Val Score Test Score
BERT-Large - CoLA Link 65.3 61.2
BERT-Large MNLI SST Link 93.9 95.1
BERT-Large MNLI MRPC Link 90.4 88.6
BERT-Large MNLI STS-B Link 90.7 89.0
BERT-Large - QQP Link 90.0 81.2
BERT-Large - MNLI Link 86.7 86.2
BERT-Large MNLI QNLI Link 92.3 92.8
BERT-Large MNLI RTE Link 84.1 79.0
BERT-Large - WNLI* N/A 56.3 65.1

Overall GLUE Score: 82.0

Models differ slightly from published results because they were retrained.

Example usage

Preparation

You will need to download the GLUE data to run our tasks. See here.

You will also need to set the two following environment variables:

  • GLUE_DIR: This should point to the location of the GLUE data downloaded from jiant.
  • BERT_ALL_DIR: Set BERT_ALL_DIR=/PATH_TO_THIS_REPO/cache/bert_metadata
    • For mor general use: BERT_ALL_DIR should point to the location of BERT downloaded from here. Importantly, the BERT_ALL_DIR needs to contain the files uncased_L-24_H-1024_A-16/bert_config.json and uncased_L-24_H-1024_A-16/vocab.txt.
Example 1: Generating Predictions

To generate validation/test predictions, as well as validation metrics, run something like the following:

export TASK=rte
export BERT_LOAD_PATH=path/to/mnli__rte.p
export OUTPUT_PATH=rte_output

python glue/train.py \
    --task_name $TASK \
    --do_val --do_test \
    --do_lower_case \
    --bert_model bert-large-uncased \
    --bert_load_mode full_model_only \
    --bert_load_path $BERT_LOAD_PATH \
    --eval_batch_size 64 \
    --output_dir $OUTPUT_PATH
Example 2: Fine-tuning from vanilla BERT

We recommend training with a batch size of 16/24/32.

export TASK=mnli
export OUTPUT_PATH=mnli_output

python glue/train.py \
    --task_name $TASK \
    --do_train --do_val --do_test --do_val_history \
    --do_save \
    --do_lower_case \
    --bert_model bert-large-uncased \
    --bert_load_mode from_pretrained \
    --bert_save_mode model_all \
    --train_batch_size 24 \
    --learning_rate 2e-5 \
    --output_dir $OUTPUT_PATH
Example 3: Fine-tuning from MNLI model
export PRETRAINED_MODEL_PATH=/path/to/mnli.p
export TASK=rte
export OUTPUT_PATH=rte_output

python glue/train.py \
    --task_name $TASK \
    --do_train --do_val --do_test --do_val_history \
    --do_save \
    --do_lower_case \
    --bert_model bert-large-uncased \
    --bert_load_path $PRETRAINED_MODEL_PATH \
    --bert_load_mode model_only \
    --bert_save_mode model_all \
    --train_batch_size 24 \
    --learning_rate 2e-5 \
    --output_dir $OUTPUT_PATH
Example 4: STILTs MNLI → RTE
export TASK_A=mnli
export TASK_B=rte
export OUTPUT_PATH_A=mnli
export OUTPUT_PATH_B=mnli__rte

# MNLI
python glue/train.py \
    --task_name $TASK_A \
    --do_train --do_val \
    --do_save \
    --do_lower_case \
    --bert_model bert-large-uncased \
    --bert_load_mode from_pretrained \
    --bert_save_mode model_all \
    --train_batch_size 24 \
    --learning_rate 2e-5 \
    --output_dir $OUTPUT_PATH_A
    
# MNLI -> RTE
python glue/train.py \
    --task_name $TASK_B \
    --do_train --do_val --do_test --do_val_history \
    --do_save \
    --do_lower_case \
    --bert_model bert-large-uncased \
    --bert_load_path $OUTPUT_PATH_A/all_state.p \
    --bert_load_mode state_model_only \
    --bert_save_mode model_all \
    --train_batch_size 24 \
    --learning_rate 2e-5 \
    --output_dir $OUTPUT_PATH_B

Submission to GLUE leaderboard

We have included helper scripts for exporting submissions to the GLUE leaderboard. To prepare for submission, copy the template from cache/submission_template to a given new output folder:

cp -R cache/submission_template /path/to/new_submission

After running a fine-tuned/pretrained model on a task with the --do_test argument, a folder (e.g. rte_output) will be created containing test_preds.csv among other files. Run the following command to convert test_preds.csv to the submission format in the output folder.

python glue/format_for_glue.py 
    --task-name rte \
    --input-base-path /path/to/rte_output \
    --output-base-path /path/to/new_submission

Once you have exported submission predictions for each task, you should have 11 .tsv files in total. If you run wc -l *.tsv, you should see something like the following:

   1105 AX.tsv
   1064 CoLA.tsv
   9848 MNLI-mm.tsv
   9797 MNLI-m.tsv
   1726 MRPC.tsv
   5464 QNLI.tsv
 390966 QQP.tsv
   3001 RTE.tsv
   1822 SST-2.tsv
   1380 STS-B.tsv
    147 WNLI.tsv
 426597 total 

Next run zip -j -D submission.zip *.tsv in the folder to generate the submission zip file. Upload the zip file to https://gluebenchmark.com/submit to submit to the leaderboard.

Extras

This repository also supports the use of Adapter layers for BERT.

FAQ

Q: What does STILTs stand for?

STILTs stand for Supplementary Training on Intermediate Labeled-data Tasks.

Q: That's it? You finetune on one task, then finetune on another?

Yes—in some sense, this is the simplest possible approach to pretrain/multi-task-train on another task. We do not perform multi-task training or balancing of multi-task losses, any additional hyperparameter search, early stopping or any other modification to the training procedure. We simply apply the standard BERT training procedure twice.

Even so, this simple method of supplementary training is still competitive with more complex multi-task methods such as MT-DNN and ALICE.

So far, we have observed three main benefits of STILTs:

  1. STILTs tends to improve performance on tasks with little data (<10k training examples)
  2. STILTs tends to stabilize training on tasks with little data
  3. In cases where the intermediate and target tasks are closely related (e.g. MNLI/RTE), we observe a significant improvement in performance.

Q: The paper/abstract mentions a GLUE score of 81.8, while the leaderboard shows 82.0. What's going on?

The GLUE benchmark underwent an update wherein QNLI(v1) was replaced by a QNLIv2, which has a different train/val/test split. The GLUE leaderboard currently reports QNLIv2 scores. On the other hand, all experiments in the paper were run on QNLIv1, so we chose to report the GLUE score based on QNLIv1 in the paper.

In short, with QNLIv1, we get a GLUE score of 81.8. With QNLIv2, we got a score of 82.0.

Q: When I run finetuning on CoLA/MRPC/STS-B/RTE, I get terrible results. Why?

Finetuning BERT-Large on tasks with little training data (<10k) tends to be unstable. This is referenced in the original BERT paper.

Q: Where are the other models (GPT, ELMo) referenced in the paper?

Those results were obtained using the jiant framework. We currently have no plans to publish the trained models for those experiments.

Citation

@article{phang2018stilts,
  title={Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks},
  author={Phang, Jason and F\'evry,, Thibault and Bowman, Samuel R.},
  journal={arXiv preprint arXiv:1811.01088v2},
  year={2018}
}
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