wehub-resource-sync
80 lines
3.2 KiB
Bash
Executable File
80 lines
3.2 KiB
Bash
Executable File
# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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###
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# This file contains script to generate saliency map of a specific baseline model and language on given input data
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# The result of this script will be used to evaluate the interpretive performance of the baseline model
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###
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export CUDA_VISIBLE_DEVICES=4
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export PYTHONPATH=./:$PYTHONPATH
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LANGUAGE=en # LANGUAGE choose in [ch, en]
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BASE_MODEL=roberta_base # BASE_MODEL choose in [roberta_base, roberta_large, lstm]
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INTER_MODE=attention # INTER_MODE choice in [attention, integrated_gradient, lime]
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TASK=senti_${LANGUAGE}
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DATA=../../data/${TASK}
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START_ID=0
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FROM_PRETRAIN='test'
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VOCAB_PATH='test'
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if [[ $LANGUAGE == "en" ]]; then
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if [[ $BASE_MODEL == "roberta_base" ]]; then
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FROM_PRETRAIN='roberta-base'
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CKPT=pretrained_models/saved_model_en/roberta_base_20211105_135732/model_10000/model_state.pdparams
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#CKPT=pretrained_models/saved_model_en/roberta_base_20211206_164443/model_10000/model_state.pdparams
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elif [[ $BASE_MODEL == "roberta_large" ]]; then
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FROM_PRETRAIN='roberta-large'
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CKPT=pretrained_models/saved_model_en/roberta_large_20211105_160323/model_4000/model_state.pdparams
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#CKPT=pretrained_models/saved_model_en/roberta_large_20211207_174631/model_4000/model_state.pdparams
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elif [[ $BASE_MODEL == "lstm" ]]; then
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VOCAB_PATH='rnn/vocab.sst2_train'
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CKPT=rnn/checkpoints_en/final.pdparams
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fi
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elif [[ $LANGUAGE == "ch" ]]; then
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if [[ $BASE_MODEL == "roberta_base" ]]; then
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FROM_PRETRAIN='roberta-wwm-ext'
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CKPT=pretrained_models/saved_model_ch/roberta_base/model_900/model_state.pdparams
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#CKPT=pretrained_models/saved_model_ch/roberta_base_20211229_101252/model_900/model_state.pdparams
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elif [[ $BASE_MODEL == "roberta_large" ]]; then
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FROM_PRETRAIN='roberta-wwm-ext-large'
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CKPT=pretrained_models/saved_model_ch/roberta_large_20211014_192021/model_900/model_state.pdparams
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#CKPT=pretrained_models/saved_model_ch/roberta_large_20211229_105019/model_900/model_state.pdparams
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elif [[ $BASE_MODEL == "lstm" ]]; then
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VOCAB_PATH='rnn/vocab.txt'
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CKPT=rnn/checkpoints_ch/final.pdparams
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fi
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fi
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OUTPUT=./output/${TASK}.${BASE_MODEL}
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[ -d $OUTPUT ] || mkdir -p $OUTPUT
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set -x
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python3 ./saliency_map/sentiment_interpretable.py \
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--language $LANGUAGE \
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--base_model $BASE_MODEL \
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--data_dir $DATA \
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--vocab_path $VOCAB_PATH \
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--from_pretrained $FROM_PRETRAIN \
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--batch_size 1 \
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--init_checkpoint $CKPT \
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--inter_mode $INTER_MODE\
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--output_dir $OUTPUT \
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--n-samples 200 \
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--start_id $START_ID \
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--eval $@
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