OpenDeltaMirror/examples/examples_seq2seq
shengdinghu b856ad0fb9 first commit 2022-02-14 21:19:03 +08:00
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README.md first commit 2022-02-14 21:19:03 +08:00
__init__.py first commit 2022-02-14 21:19:03 +08:00
collect_result.jsonl first commit 2022-02-14 21:19:03 +08:00
run.sh first commit 2022-02-14 21:19:03 +08:00
run_seq2seq.py first commit 2022-02-14 21:19:03 +08:00
seq2seq_trainer.py first commit 2022-02-14 21:19:03 +08:00

README.md

Appling OpenDelta to GLUE/SuperGLUE tasks using Seq2Seq Paradigm

install the repo

cd ../
python setup_seq2seq.py develop

This will add examples_seq2seq to the environment path of the python lib.

Generating the json configuration file

python config_gen.py --job $job_name

The available job configuration (e.g., --job lora_t5-base) can be seen from config_gen.py. You can also create your only configuration.

Run the code

python run_seq2seq.py configs/$job_name/$dataset.json

Possible Errors

ValueError: You must login to the Hugging Face hub on this computer by typing `transformers-cli login` and entering your credentials to use `use_auth_token=Tr
ue`. Alternatively, you can pass your own token as the `use_auth_token` argument.
  • Solution 1: Please register an account on HuggingFace Then run transformers-cli login on your command line to enter the username and password.

  • Solution 2: Disable push_to_hub by modifying in the config.json : "push_to_hub": False

OSError: Looks like you do not have git-lfs installed, please install. You can install from https://git-lfs.github.com/. Then run `git lfs install` (you only have to do this once).
  • Solution 1:
wget -P ~ https://github.com/git-lfs/git-lfs/releases/download/v3.0.2/git-lfs-linux-amd64-v3.0.2.tar.gz
cd ~
tar -xvzf git-lfs-linux-amd64-v3.0.2.tar.gz
export PATH=~:$PATH
git-lfs install
  • Solution 2: Disable push_to_hub by modifying in the config.json : "push_to_hub": False
  1. dataset connection error

Solution 1: open a python console, running the error command again, may not be useful

Solution 2: download the dataset by yourself on a internect connected machine, saved to disk and transfer to your server, at last load_from_disk.

This example repo is based on the compacter training scripts, with compacter-related lines removed. Thanks to the authors of the original repo. In addition, in private correspondence with the authors, they shared the codes to create the json configs. Thanks again for their efforts.