Splinter-paddle

1. Introduction

• This repoitory contains a paddle implementation of the Splinter, which is a new question answering pretraining scheme based on few-shot learning. It comes from the 2021 ACL paper Few-Shot Question Answering by Pretraining Span Selection and uses recurring span selection to enhance the performance.
• Paper: "Few-Shot Question Answering by Pretraining Span Selection", to appear at ACL 2021.
• Original Pytorch Implementation: oriram/splinter (github.com)
• Dataset: SQuAD

2. Experiment Results

We experimented with three few-shot scenarios (16, 128, 1024 examples) using classical dataset SQuAD, which is a representative part of the experiment in the paper.

	16 examples F1	128 examples F1	1024 examples F1
Original Paper's Results	54.6	72.7	82.8
Ours Results	55.62	72.63	82.89

• Ours More Detailed Results:

seed selection of train files	16 examples F1	128 examples F1	1024 examples F1
42	54.38	71.45	82.46
43	51.89	72.42	82.82
44	61.68	73.81	83.22
45	45.14	72.43	83.39
46	58.92	73.06	82.57
Average	55.62	72.63	82.89

We can find the instability of few-shot learning. The more samples in the training set, the more stable the results will be, which is consistent with our intuition.

3. Code Structure

• align_works: Our all align works about paddleimplementation;
  • 1_check_forward: the align works about the forward training;
  • 2_check_devdata_testdata: the align works about dev dataset and test dataset;
  • 3_check_metirc: the align works about metric;
  • ......
  • 10_check_network_params_init: the align works about model's parameters init;
• finetuning: finetuning codes of Splinter using paddle framework;
• mrqa-few-shot/squad: SQuAD dataset;
• output_avg: the experiment results;
• paddlenlp: changed edition by us based on paddlepaddle/paddlenlp;
• reprod_log: a third-party library using checking precision between torch's codes and paddle's codes;
• splinter_init: model's params and configs.

4. Environment

• Hardward: GPU, CPU
• Framework:
  • PaddlePaddle >= 2.0.0

5. Quick start

5.1 Run Style

1. Git clone this repo and download model parameters from Google Cloud share
   1. splinter ---> align_works/splinter
   2. splinter_init ---> splinter_init
2. Runing our codes in BaiDu AI Studio. Choosing Splinter-paddle edition from this link and runing the program.

We suggest you choose the AI Studio.

5.2 Run Scripts

• We can obtain the average experiment results by the script that can run all of the sampled datasets.

python splinter-paddle/finetuning/run_all.py \
    --model_type=bert \
    --model_name_or_path="splinter-paddle/splinter_init" \
    --qass_head=True \
    --tokenizer_name="splinter-paddle/splinter_init" \
    --output_dir="output" \
    --output_dir_avg="output_avg" \
    --train_file="" \
    --predict_file="splinter-paddle/mrqa-few-shot/squad/dev_qass.jsonl" \
    --do_train \
    --do_eval \
    --max_seq_length=384 \
    --doc_stride=128 \
    --threads=4 \
    --save_steps=50000 \
    --per_gpu_train_batch_size=12 \
    --per_gpu_eval_batch_size=16 \
    --learning_rate=3e-5 \
    --max_answer_length=10 \
    --warmup_ratio=0.1 \
    --min_steps=200 \
    --num_train_epochs=10 \
    --seed=128 \
    --use_cache=False \
    --evaluate_every_epoch=False \
    --initialize_new_qass=False

• We can obtain a experiment result of a single sampled dataset by this script.

python splinter-paddle/finetuning/run.py \
    --model_type=bert \
    --model_name_or_path="splinter-paddle/splinter_init" \
    --qass_head=True \
    --tokenizer_name="splinter-paddle/splinter_init" \
    --output_dir="output_single" \
    --train_file="splinter-paddle/mrqa-few-shot/squad/squad-train-seed-42-num-examples-16_qass.jsonl" \
    --predict_file="splinter-paddle/mrqa-few-shot/squad/dev_qass.jsonl" \
    --do_train \
    --do_eval \
    --max_seq_length=384 \
    --doc_stride=128 \
    --threads=1 \
    --save_steps=50000 \
    --per_gpu_train_batch_size=12 \
    --per_gpu_eval_batch_size=16 \
    --learning_rate=3e-5 \
    --max_answer_length=10 \
    --warmup_ratio=0.1 \
    --min_steps=200 \
    --num_train_epochs=10 \
    --seed=128 \
    --use_cache=False \
    --evaluate_every_epoch=False \
    --initialize_new_qass=False