This is the implementation of the paper Making Pre-trained Language Models Better Few-shot Learners. LM-BFF is short for better few-shot fine-tuning of language models.
In this work we present LM-BFF, a suite of simple and complementary techniques for fine-tuning pre-trained language models on a small number of training examples. Our approach includes:
- Prompt-based fine-tuning together with a novel pipeline for automating prompt generation.
- A refined strategy for incorporating demonstrations into context.
You can find more details of this work in our paper.
To run our code, please install all the dependency packages by using the following command:
pip install -r requirements.txt
NOTE: Different versions of packages (like pytorch, transformers, etc.) may lead to different results from the paper. However, the trend should still hold no matter what versions of packages you use.
We pack the original datasets (SST-2, SST-5, MR, CR, MPQA, Subj, TREC, CoLA, MNLI, SNLI, QNLI, RTE, MRPC, QQP, STS-B) here. Please download it and extract the files to ./data/original, or run the following commands:
cd data
bash download_dataset.shThen use the following command (in the root directory) to generate the few-shot data we need:
python tools/generate_k_shot_data.pySee tools/generate_k_shot_data.py for more options. For results in the paper, we use the default options: we take K=16 and take 5 different seeds of 13, 21, 42, 87, 100. The few-shot data will be generated to data/k-shot. In the directory of each dataset, there will be folders named as $K-$SEED indicating different dataset samples. You can use the following command to check whether the generated data are exactly the same as ours:
cd data/k-shot
md5sum -c checksumNOTE: During training, the model will generate/load cache files in the data folder. If your data have changed, make sure to clean all the cache files (starting with "cache").
Our code is built on transformers. We did all our experiments with version 3.4.0, but it should work with other versions.
Before running any experiments, create the result folder by mkdir result to save checkpoints. Then you can run our code with the following example:
python run.py \
--task_name SST-2 \
--data_dir data/k-shot/SST-2/16-42 \
--overwrite_output_dir \
--do_train \
--do_eval \
--do_predict \
--evaluate_during_training \
--model_name_or_path roberta-large \
--few_shot_type prompt-demo \
--num_k 16 \
--max_steps 1000 \
--eval_steps 100 \
--per_device_train_batch_size 2 \
--learning_rate 1e-5 \
--num_train_epochs 0 \
--output_dir result/tmp \
--seed 42 \
--template "*cls**sent_0*_It_was*mask*.*sep+*" \
--mapping "{'0':'terrible','1':'great'}" \
--num_sample 16 \Most arguments are inherited from transformers and are easy to understand. We further explain some of the LM-BFF's arguments:
few_shot_type: There are three modesfinetune: Standard fine-tuningprompt: Prompt-based fine-tuning.prompt-demo: Prompt-based fine-tuning with demonstrations.
num_k: Number of training instances for each class. We takenum_k=16 in our paper. This argument is mainly used for indexing logs afterwards (because the training example numbers are actually decided by the data split you use).template: Template for prompt-based fine-tuning. We will introduce the template format later.mapping: Label word mapping for prompt-based fine-tuning. It is a string of dictionary indicating the mapping from label names to label words. NOTE: For RoBERTa, the model will automatically add space before the word. See the paper appendix for details.num_sample: When using demonstrations during inference, the number of samples for each input query. Saynum_sample=16, then we sample 16 different sets of demonstrations for one input, do the forward seperately, and average the logits for all 16 samples as the final prediction.
Also, this codebase supports BERT-series and RoBERTa-series pre-trained models in Huggingface's transformers. You can check Huggingface's website for available models and pass models with a "bert" or "roberta" in their names to --model_name_or_path. Some examples would be bert-base-uncased, bert-large-uncased, roberta-base, roberta-large, etc.
To easily run our experiments, you can also use run_experiment.sh (this command runs prompt-based fine-tuning with demonstrations, no filtering, manual prompt):
TAG=exp TYPE=prompt-demo TASK=SST-2 BS=2 LR=1e-5 SEED=42 MODEL=roberta-large bash run_experiment.shWe have already defined the templates and label word mappings in it, so you only need manipulate several hyper-parameters and TAG (you can use whatever tag you want and it just makes finding results easier). See run_experiment.sh for more options of these environment variables. Besides, you can add extra arguments by
TAG=exp TYPE=prompt-demo TASK=SST-2 BS=2 LR=1e-5 SEED=42 MODEL=roberta-large bash run_experiment.sh "--output_dir result/exp --max_seq_length 512"To carry out experiments with multiple data splits, as the evaluation protocol detailed in $3.3 of our paper (grid-search for each seed and aggregate the results over 5 different seeds), you can use the following scripts:
for seed in 13 21 42 87 100
do
for bs in 2 4 8
do
for lr in 1e-5 2e-5 5e-5
do
TAG=exp \
TYPE=prompt-demo \
TASK=SST-2 \
BS=$bs \
LR=$lr \
SEED=$seed \
MODEL=roberta-large \
bash run_experiment.sh
done
done
doneAll the results will be stored in ./log. To gather all the results, run the following command:
python tools/gather_result.py --condition "{'tag': 'exp', 'task_name': 'sst-2', 'few_shot_type': 'prompt-demo'}"Then the program will find all the trials that satisfy the condition in ./log, and print the mean/std of the final results. Note that the task names are all lower-cased and if the task has more than one metric, you need to specify the major metric (used for taking the best validation trial) in the name (e.g., mnli, mnli-mm, mrpc/acc, mrpc/f1, qqp/acc, qqp/f1, sts-b/pearson, sts-b/spearman).
To use the filtering mechanism when using demonstrations, we need to first generate Sentence-BERT embeddings. To generate embeddings for datasets in our paper, you can directly run
bash tools/get_sbert_embedding.sh roberta-large
roberta-large can also be replaced by bert-base, bert-large, roberta-base and distilbert-base (see Sentence Transformers for details). See tools/get_sbert_embedding.sh and tools/get_sbert_embedding.py if you want to add more datasets.
After generating the embeddings (embeddings are saved as numpy files in the data folders), we can run the following commands to do prompt-based fine-tuning with demonstrations with filtering:
TAG=exp TYPE=prompt-demo TASK=SST-2 BS=2 LR=1e-5 SEED=42 MODEL=roberta-large bash run_experiment.sh "--demo_filter --demo_filter_model sbert-roberta-large"We provide our automatic search results in auto_template and auto_label_mapping. There are three types of files:
SST-2/16-42.txt: Initial search results for SST-2 dataset, K=16 and SEED=42.SST-2/16-42.sort.txt: Do prompt-based fine-tuning on initial results and sort them based on dev set performance.SST-2/16-42.score.txt: Same as above, but with dev set scores.
To use the best automatic template (auto-T in the paper), use the following command:
TAG=exp TYPE=prompt-demo TASK=SST-2 BS=2 LR=1e-5 SEED=42 MODEL=roberta-large bash run_experiment.sh "--template_path auto_template/SST-2/16-42.sort.txt --template_id 0"You can also use the i-th automatic result by specifying different template_id.
Similarly, to use automatic label (auto-L in the paper), use the following command:
TAG=exp TYPE=prompt-demo TASK=SST-2 BS=2 LR=1e-5 SEED=42 MODEL=roberta-large bash run_experiment.sh "--mapping_path auto_label_mapping/SST-2/16-42.sort.txt --mapping_id 0"NOTE: Make sure to use the corresponding automatic search results with different data split seeds.
Our final results (LM-BFF) take prompt-based fine-tuning with demonstrations, filtering and automatic template, for example:
for seed in 13 21 42 87 100
do
for bs in 2 4 8
do
for lr in 1e-5 2e-5 5e-5
do
TAG=LM-BFF \
TYPE=prompt-demo \
TASK=SST-2 \
BS=$bs \
LR=$lr \
SEED=$seed \
MODEL=roberta-large \
bash run_experiment.sh "--template_path auto_template/SST-2/16-$seed.sort.txt --template_id 0 --demo_filter --demo_filter_model sbert-roberta-large"
done
done
done
python tools/gather_result.py --condition "{'tag': 'LM-BFF', 'task_name': 'sst-2', 'few_shot_type': 'prompt-demo'}"If you want to try automatically generating templates by yourself, here are the instructions. Note that it is an extremely long process :)
To get automatic templates, we first generate template candidates by using T5:
python tools/generate_template.py \
--output_dir my_auto_template \
--task_name SST-2 \
--seed 13 21 42 87 100 \
--t5_model t5-3b \
--beam 100Where --t5_model specifies the pre-trained T5 checkpoint to use and --beam specifies the beam search width. Note that t5-3b model will take approximately 15GB GPU memory, and if your GPU does not support it, you can try smaller T5 models (e.g., t5-base).
Then we do prompt-based fine-tuning of all the templates
for template_id in {0..99}
do
for seed in 13 21 42 87 100
do
# To save time, we fix these hyper-parameters
bs=8
lr=1e-5
# Since we only use dev performance here, use --no_predict to skip testing
TAG=exp-template \
TYPE=prompt \
TASK=SST-2 \
BS=$bs \
LR=$lr \
SEED=$seed \
MODEL=roberta-large \
bash run_experiment.sh "--template_path my_auto_template/SST-2/16-$seed.txt --template_id $template_id --no_predict"
done
done... and sort them based on dev set performance:
python tools/sort_template.py --condition "{'tag': 'exp-template', 'task_name': 'sst-2'}" --template_dir my_auto_templateThe sorted results will be saved in my_auto_template, with the same format as described in Automatically searched prompt.
Similar to the process of automatic template search, we first generate candidate label word mappings by running:
bash tools/run_generate_labels.shYou can modify the options in tools/run_generate_labels.sh to run this for different datasets or save mappings to different directories. After running the generation, the candidate label mappings will be saved in my_auto_label_mapping/manual_template.
Then we do prompt-based fine-tuning of all the mappings by:
for mapping_id in {0..99}
do
for seed in 13 21 42 87 100
do
# To save time, we fix these hyper-parameters
bs=8
lr=1e-5
# Since we only use dev performance here, use --no_predict to skip testing
TAG=exp-mapping \
TYPE=prompt \
TASK=SST-2 \
BS=$bs \
LR=$lr \
SEED=$seed \
MODEL=roberta-large \
bash run_experiment.sh "--mapping_path my_auto_label_mapping/manual_template/SST-2/16-$seed.txt --mapping_id $mapping_id --no_predict"
done
done... and sort them based on dev set performance:
python tools/sort_mapping.py --condition "{'tag': 'exp-mapping', 'task_name': 'sst-2'}" --mapping_dir my_auto_label_mapping/manual_templateThe sorted results will be saved in my_auto_label_mapping/manual_template, with the same format as described in Automatically searched prompt.
Auto T + L: We can also do a joint search of templates and label word mappings following these steps:
- First, do the automatic template search following Search for automatic templates.
- The following steps are similar to automatic label mapping except a few arguments. When running
tools/run_generate_labels.sh, changeLOAD_TEMPLATEStotruein it and the template + mapping candidates will be written inmy_auto_label_mapping/auto_template - For the following fine-tuning, change
--mapping_pathand--mapping_idto--prompt_pathand--prompt_id. - In the end, for re-ranking all the prompts, change
tools/sort_mapping.pytotools/sort_prompt.pyto get the final lists.
First we need to train models with different templates:
mkdir ensemble_predict_results
for template_id in {0..19} # Use top 20 templates
do
array_id=0
for seed in 13 21 42 87 100
do
for bs in 2 4 8
do
for lr in 1e-5 2e-5 5e-5
do
TAG=exp-ensemble \
TYPE=prompt-demo \
TASK=SST-2 \
BS=$bs \
LR=$lr \
SEED=$seed \
MODEL=roberta-large \
bash run_experiment.sh "--template_path auto_template/SST-2/16-$seed.sort.txt --template_id $template_id --model_id $template_id --array_id $array_id --save_logit --save_logit_dir ensemble_predict_results"
array_id=$(expr $array_id + 1)
done
done
done
doneLooks a little complicated? It's actually pretty easy to understand: --model_id and --array_id is used to distinguish different runs, and --save_logit tells the program to save the prediction results for ensemble.
After finishing the experiments, use the following command to get the ensemble results:
python tools/ensemble.py --condition "{'tag': 'exp-ensemble', 'task_name': 'sst-2', 'few_shot_type': 'prompt-demo'}" --n_models 20where --n_models specify how many models you want to use for ensemble (should be kept the same as the number of templates you use in experiments).
It's easy to run zero-shot experiments: just add the --no_train argument:
TAG=zero-shot TYPE=prompt TASK=SST-2 BS=2 LR=1e-5 SEED=42 MODEL=roberta-large bash run_experiment.sh "--no_train"To do "GPT-3 style" in-context learning:
TAG=gpt3-in-context TYPE=prompt-demo TASK=SST-2 BS=2 LR=1e-5 SEED=42 MODEL=roberta-large bash run_experiment.sh "--no_train --num_sample 1 --gpt3_in_context_head --gpt3_in_context_num 32 --truncate_head --use_full_length"Here are two template examples:
For SST-2: *cls**sent_0*_It_was*mask*.*sep+* => [CLS] {S0} It was [MASK]. [SEP]
For MNLI: *cls**sent-_0*?*mask*,*+sentl_1**sep+* => [CLS] {S0}? [MASK], {S1} [SEP]
The template is composed of special tokens and variables (surrounded by *) and text (e.g., It_was, where space is replaced by _). Special tokens and variables contain:
*cls*,*sep*,*sep+*and*mask*: Special tokens of CLS, SEP and MASK (different for different pre-trained models and tokenizers).*sep+*means the contents before and after this token have different segment embeddings (only for BERT).*sent_i*: The i-th sentence.*sent-_i*: The i-th sentence, discarding the last character.*sentl_i*: The i-th sentence, lower-casing the first letter.*sentl-_i*: The i-th sentence, discarding the last character and lower-casing the first letter.*+sent_i*: The i-th sentence, adding an extra space at the beginning.*+sentl_i*: The i-th sentence, adding an extra space at the beginning and lower-casing the first letter.
If you have any questions related to the code or the paper, feel free to email Tianyu (tianyug@cs.princeton.edu). If you encounter any problems when using the code, or want to report a bug, you can open an issue. Please try to specify the problem with details so we can help you better and quicker!
Please cite our paper if you use LM-BFF in your work:
@article{gao2020making,
title={Making Pre-trained Language Models Better Few-shot Learners},
author={Gao, Tianyu and Fisch, Adam and Chen, Danqi},
journal={arXiv preprint arXiv:2012.15723},
year={2020}
}