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_flax_ner.py | ||
Token classification examples
Fine-tuning the library models for token classification task such as Named Entity Recognition (NER), Parts-of-speech tagging (POS) or phrase extraction (CHUNKS). The main script run_flax_ner.py leverages the 🤗 Datasets library. You can easily customize it to your needs if you need extra processing on your datasets.
It will either run on a datasets hosted on our hub or with your own text files for training and validation, you might just need to add some tweaks in the data preprocessing.
The following example fine-tunes BERT on CoNLL-2003:
python run_flax_ner.py \
--model_name_or_path bert-base-cased \
--dataset_name conll2003 \
--max_seq_length 128 \
--learning_rate 2e-5 \
--num_train_epochs 3 \
--per_device_train_batch_size 4 \
--output_dir ./bert-ner-conll2003 \
--eval_steps 300 \
--push_to_hub
Using the command above, the script will train for 3 epochs and run eval after each epoch.
Metrics and hyperparameters are stored in Tensorflow event files in --output_dir.
You can see the results by running tensorboard in that directory:
$ tensorboard --logdir .
or directly on the hub under Training metrics.
sample Metrics - tfhub.dev