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:
acc394c4f5/examples/pytorch/language-modeling/run_clm.py.
* Result of black 23.1
* Update target to Python 3.7
* Switch flake8 to ruff
* Configure isort
* Configure isort
* Apply isort with line limit
* Put the right black version
* adapt black in check copies
* Fix copies
* Finished QA example
* Dodge a merge conflict
* Update text classification and LM examples
* Update NER example
* New Keras metrics WIP, fix NER example
* Update NER example
* Update MC, summarization and translation examples
* Add XLA warnings when shapes are variable
* Make sure batch_size is consistently scaled by num_replicas
* Add PushToHubCallback to all models
* Add docs links for KerasMetricCallback
* Add docs links for prepare_tf_dataset and jit_compile
* Correct inferred model names
* Don't assume the dataset has 'lang'
* Don't assume the dataset has 'lang'
* Write metrics in text classification
* Add 'framework' to TrainingArguments and TFTrainingArguments
* Export metrics in all examples and add tests
* Fix training args for Flax
* Update command line args for translation test
* make fixup
* Fix accidentally running other tests in fp16
* Remove do_train/do_eval from run_clm.py
* Remove do_train/do_eval from run_mlm.py
* Add tensorflow tests to circleci
* Fix circleci
* Update examples/tensorflow/language-modeling/run_mlm.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update examples/tensorflow/test_tensorflow_examples.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update examples/tensorflow/translation/run_translation.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Update examples/tensorflow/token-classification/run_ner.py
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Fix save path for tests
* Fix some model card kwargs
* Explain the magical -1000
* Actually enable tests this time
* Skip text classification PR until we fix shape inference
* make fixup
Co-authored-by: Joao Gante <joaofranciscocardosogante@gmail.com>
* Migrate metric to Evaluate library in tf examples
Currently tensorflow examples use `load_metric` function from Datasets
library , commit migrates function call to `load` function to
Evaluate library.
Fix for #18306
* Migrate metric to Evaluate library in tf examples
Currently tensorflow examples use `load_metric` function from Datasets
library , commit migrates function call to `load` function to
Evaluate library.
Fix for #18306
* Migrate `metric` to Evaluate for all tf examples
Currently tensorflow examples use `load_metric` function from Datasets
library , commit migrates function call to `load` function to
Evaluate library.
* Add examples telemetry
* Alternative approach
* Add to all other examples
* Add to templates as well
* Put framework separately
* Same for TensorFlow
