A newer version of the Gradio SDK is available:
5.8.0
Better Fine-Tuning by Reducing Representational Collapse
This repo contains the code to replicate all experiments from the Better Fine-Tuning by Reducing Representational Collapse paper excluding the probing results.
The R3F sentence prediction criterion is registered as sentence_prediction_r3f
while the label smoothing version of it is implemented as label_smoothed_cross_entropy_r3f
. The R4F version of the sentence prediction criterion can be achieved by applying spectral norm to the classification head via the --spectral-norm-classification-head
parameter.
Hyper-parameters
Our methods introduce 3 new hyper-parameters; --eps
which sets the standard deviation or range of the distribution we're sampling from, --r3f-lambda
which controls the combining of logistic loss and noisy KL loss and --noise-type
which controls which parametric distribution we use ('normal', 'uniform').
For example to run R3F on RTE from GLUE
TOTAL_NUM_UPDATES=3120
WARMUP_UPDATES=187
LR=1e-05
NUM_CLASSES=2
MAX_SENTENCES=8 # Batch size.
ROBERTA_PATH=/path/to/roberta/model.pt
CUDA_VISIBLE_DEVICES=0 fairseq-train RTE-bin \
--restore-file $ROBERTA_PATH \
--max-positions 512 \
--max-sentences $MAX_SENTENCES \
--max-tokens 4400 \
--task sentence_prediction \
--reset-optimizer --reset-dataloader --reset-meters \
--required-batch-size-multiple 1 \
--init-token 0 --separator-token 2 \
--arch roberta_large \
--criterion sentence_prediction_r3f \
--num-classes $NUM_CLASSES \
--dropout 0.1 --attention-dropout 0.1 \
--weight-decay 0.1 --optimizer adam --adam-betas "(0.9, 0.98)" --adam-eps 1e-06 \
--clip-norm 0.0 \
--lr-scheduler polynomial_decay --lr $LR --total-num-update $TOTAL_NUM_UPDATES --warmup-updates $WARMUP_UPDATES \
--fp16 --fp16-init-scale 4 --threshold-loss-scale 1 --fp16-scale-window 128 \
--max-epoch 10 \
--find-unused-parameters \
--best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \
--noise-type uniform --r3f-lambda 0.7 \
--user-dir examples/rxf/rxf_src
Citation
@article{aghajanyan2020better,
title={Better Fine-Tuning by Reducing Representational Collapse},
author={Aghajanyan, Armen and Shrivastava, Akshat and Gupta, Anchit and Goyal, Naman and Zettlemoyer, Luke and Gupta, Sonal},
journal={arXiv preprint arXiv:2008.03156},
year={2020}
}