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* Reorganize example folder * Continue reorganization * Change requirements for tests * Final cleanup * Finish regroup with tests all passing * Copyright * Requirements and readme * Make a full link for the documentation * Address review comments * Apply suggestions from code review Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Add symlink * Reorg again * Apply suggestions from code review Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com> * Adapt title * Update to new strucutre * Remove test * Update READMEs Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Thomas Wolf <thomwolf@users.noreply.github.com>
40 lines
1.5 KiB
Bash
40 lines
1.5 KiB
Bash
# Copyright 2020 The HuggingFace Team. 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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export WANDB_PROJECT=distilbart-trainer
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export BS=32
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export m=sshleifer/student_cnn_12_6
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export tok=facebook/bart-large
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export MAX_TGT_LEN=142
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python finetune_trainer.py \
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--model_name_or_path $m --tokenizer_name $tok \
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--data_dir cnn_dm \
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--output_dir distilbart-cnn-12-6 --overwrite_output_dir \
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--learning_rate=3e-5 \
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--warmup_steps 500 --sortish_sampler \
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--fp16 \
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--n_val 500 \
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--gradient_accumulation_steps=1 \
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--per_device_train_batch_size=$BS --per_device_eval_batch_size=$BS \
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--freeze_encoder --freeze_embeds \
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--num_train_epochs=2 \
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--save_steps 3000 --eval_steps 3000 \
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--logging_first_step \
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--max_target_length 56 --val_max_target_length $MAX_TGT_LEN --test_max_target_length $MAX_TGT_LEN \
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--do_train --do_eval --do_predict \
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--evaluation_strategy steps \
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--predict_with_generate --sortish_sampler \
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"$@"
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