Spaces:
Sleeping
Sleeping
File size: 4,166 Bytes
6fc683c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 |
#!/bin/bash
# Copyright 2020 Google and DeepMind.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
REPO=$PWD
MODEL=${1:-"xlm-roberta-base"}
STAGE=${2:-1}
GPU=${3:-0}
DATA_DIR=${4:-"$REPO/download/"}
OUT_DIR=${5:-"$REPO/outputs/"}
SEED=${6:-1}
export CUDA_VISIBLE_DEVICES=$GPU
cp -r $DATA_DIR/squad/ $DATA_DIR/mlqa/squad1.1/
TASK='mlqa'
TRANSLATION_PATH=$DATA_DIR/xtreme_translations/SQuAD/translate-train/
MODEL_PATH=$DATA_DIR/$MODEL
EPOCH=4
MAXL=384
LANGS="en,es,de,ar,hi,vi,zh"
BSR=0.3
SA=0.3
SNBS=-1
CSR=0.3
R1_LAMBDA=5.0
R2_LAMBDA=5.0
if [ $MODEL == "xlm-roberta-large" ]; then
BATCH_SIZE=4
GRAD_ACC=8
LR=1.5e-5
else
BATCH_SIZE=32
GRAD_ACC=1
LR=3e-5
fi
if [ $STAGE == 1 ]; then
OUTPUT_DIR="${OUT_DIR}/${TASK}/${MODEL}-LR${LR}-epoch${EPOCH}-MaxLen${MAXL}-CS-csr${CSR}-R1_LAMBDA${R1_LAMBDA}/"
python ./src/run_qa.py --model_type xlmr \
--task_name $TASK \
--model_name_or_path $MODEL_PATH \
--do_train \
--do_eval \
--language $LANGS \
--train_language en \
--data_dir $DATA_DIR/$TASK/ \
--per_gpu_train_batch_size $BATCH_SIZE \
--gradient_accumulation_steps $GRAD_ACC \
--per_gpu_eval_batch_size 128 \
--learning_rate $LR \
--num_train_epochs $EPOCH \
--save_steps 0 \
--logging_each_epoch \
--max_seq_length $MAXL \
--doc_stride 128 \
--output_dir $OUTPUT_DIR \
--overwrite_output_dir \
--evaluate_during_training \
--logging_steps 50 \
--evaluate_steps 0 \
--seed $SEED \
--fp16 --fp16_opt_level O2 \
--warmup_steps -1 \
--enable_r1_loss \
--r1_lambda $R1_LAMBDA \
--original_loss \
--overall_ratio 1.0 \
--keep_boundary_unchanged \
--enable_code_switch \
--code_switch_ratio $CSR \
--dict_dir $DATA_DIR/dicts \
--dict_languages es,de,ar,hi,vi,zh \
--noised_max_seq_length $MAXL
elif [ $STAGE == 2 ]; then
FIRST_STAGE_MODEL_PATH="${OUT_DIR}/${TASK}/${MODEL}-LR${LR}-epoch${EPOCH}-MaxLen${MAXL}-CS-csr${CSR}-R1_LAMBDA${R1_LAMBDA}/"
OUTPUT_DIR="${OUT_DIR}/${TASK}/${MODEL}-LR${LR}-epoch${EPOCH}-MaxLen${MAXL}-SS-bsr${BSR}-sa${SA}-snbs${SNBS}-R1_Lambda${R1_LAMBDA}-Aug1.0-SS-R2_Lambda${R2_LAMBDA}/"
python ./src/run_qa.py --model_type xlmr \
--task_name $TASK \
--model_name_or_path $MODEL_PATH \
--do_train \
--do_eval \
--language $LANGS \
--train_language en \
--data_dir $DATA_DIR/$TASK/ \
--per_gpu_train_batch_size $BATCH_SIZE \
--gradient_accumulation_steps $GRAD_ACC \
--per_gpu_eval_batch_size 128 \
--learning_rate $LR \
--num_train_epochs $EPOCH \
--save_steps 0 \
--logging_each_epoch \
--max_seq_length $MAXL \
--doc_stride 128 \
--output_dir $OUTPUT_DIR \
--overwrite_output_dir \
--evaluate_during_training \
--logging_steps 50 \
--evaluate_steps 0 \
--seed $SEED \
--fp16 --fp16_opt_level O2 \
--warmup_steps -1 \
--enable_r1_loss \
--r1_lambda $R1_LAMBDA \
--original_loss \
--overall_ratio 1.0 \
--keep_boundary_unchanged \
--enable_bpe_sampling \
--bpe_sampling_ratio $BSR \
--sampling_alpha $SA \
--sampling_nbest_size $SNBS \
--noised_max_seq_length $MAXL \
--enable_data_augmentation \
--augment_ratio 1.0 \
--augment_method ss \
--max_steps 24000 \
--r2_lambda $R2_LAMBDA \
--first_stage_model_path $FIRST_MODEL_PATH
fi
|