# Good command to test examples/seq2seq refactors

**URL:** <https://discuss.huggingface.co/t/good-command-to-test-examples-seq2seq-refactors/982>\
**Category:** 🤗Transformers\
**Created:** [September 3, 2020, 8:02pm UTC](https://discuss.huggingface.co/t/good-command-to-test-examples-seq2seq-refactors/982 "2020-09-03T20:02:27Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![sshleifer](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/sshleifer/32/3304_2.png) [@sshleifer](https://discuss.huggingface.co/u/sshleifer)\
**Post date:** [September 3, 2020, 8:02pm UTC](https://discuss.huggingface.co/t/good-command-to-test-examples-seq2seq-refactors/982/1 "2020-09-03T20:02:28Z")

</div>

Setup:

```auto
pip install -r examples/requirements.txt
pip install torch==1.5.1

```

You can test torch 1.6, but current code does not work well w torch 1.6.

Fetch data for wmt\_en\_ro

```auto
wget https://s3.amazonaws.com/datasets.huggingface.co/translation/wmt_en_ro.tar.gz
tar -xzvf wmt_en_ro.tar.gz

```

Finetune 3 layer decoder on wmt\_en\_ro

```auto
export PYTHONPATH="../":"${PYTHONPATH}"
export WANDB_PROJECT=nate
export BS=64
export m=m63=sshleifer/student_marian_en_ro_6_3
export MAX_LEN=128
python finetune.py \
  --learning_rate=3e-4 \
  --do_train \
  --do_predict \
  --fp16 \
  --val_check_interval 0.25 \
  --data_dir wmt_en_ro \
  --max_source_length $MAX_LEN --max_target_length $MAX_LEN --val_max_target_length $MAX_LEN --test_max_target_length $MAX_LEN \
  --freeze_encoder --freeze_embeds --num_train_epochs=6 \
  --train_batch_size=$BS --eval_batch_size=$BS \
  --tokenizer_name $m --model_name_or_path $m \
  --warmup_steps 500 --sortish_sampler --logger_name wandb \
  --gpus 1 --fp16_opt_level=O1 --task translation --label_smoothing 0.1 \
  "$@"

```

This should get val\_avg\_bleu \> 24 within an hour. One epoch should take \< 1 hour.

cc @nateraw