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95091e1582
While using `run_clm.py`,[^1] I noticed that some files were being added
to my global cache, not the local cache. I set the `cache_dir` parameter
for the one call to `evaluate.load()`, which partially solved the
problem. I figured that while I was fixing the one script upstream, I
might as well fix the problem in all other example scripts that I could.
There are still some files being added to my global cache, but this
appears to be a bug in `evaluate` itself. This commit at least moves
some of the files into the local cache, which is better than before.
To create this PR, I made the following regex-based transformation:
`evaluate\.load\((.*?)\)` -> `evaluate\.load\($1,
cache_dir=model_args.cache_dir\)`. After using that, I manually fixed
all modified files with `ruff` serving as useful guidance. During the
process, I removed one existing usage of the `cache_dir` parameter in a
script that did not have a corresponding `--cache-dir` argument
declared.
[^1]: I specifically used `pytorch/language-modeling/run_clm.py` from
v4.34.1 of the library. For the original code, see the following URL:
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|---|---|---|
| .. | ||
| README.md | ||
| requirements.txt | ||
| run_summarization.py | ||
Summarization example
This script shows an example of training a summarization model with the 🤗 Transformers library. For straightforward use-cases you may be able to use these scripts without modification, although we have also included comments in the code to indicate areas that you may need to adapt to your own projects.
Multi-GPU and TPU usage
By default, these scripts use a MirroredStrategy and will use multiple GPUs effectively if they are available. TPUs
can also be used by passing the name of the TPU resource with the --tpu argument.
Example command
python run_summarization.py \
--model_name_or_path facebook/bart-base \
--dataset_name cnn_dailymail \
--dataset_config "3.0.0" \
--output_dir /tmp/tst-summarization \
--per_device_train_batch_size 8 \
--per_device_eval_batch_size 16 \
--num_train_epochs 3 \
--do_train \
--do_eval