Hopefully it works for you, getting run_mlm.py to work took me some trial and error the first time. There is a --help option for the command line I believe. Some of the things aren't really intuitive

Then I gave that folder a name.

Oh, I forgot to mention: pip install tensorboard also

TB = Tensorboard? No idea, I haven't tried to run it with tensorboard specifically. I do have tensorboard installed in the environment, I can confirm that.

I know it's running these lines, which get defined in https://github.com/huggingface/transformers/blob/f6e254474cb4f90f8a168a599b9aaf3544c37890/src/transformers/trainer_pt_utils.py#L828
trainer.log_metrics("eval", metrics) trainer.save_metrics("eval", metrics)

Hi SmallDeer34
Can you see it in TB ? and if so where ?

AgitatedDove14 yes I see the scalars. Attached screenshot

Code to reproduce: I'll try to come up with a sample you will be able to run. But the code we're using is basically just https://github.com/huggingface/transformers/blob/f6e254474cb4f90f8a168a599b9aaf3544c37890/examples/pytorch/language-modeling/run_mlm.py

This should work. It has the tokenizer files, the train.txt, the validation.txt and a config.json

quick question:
CLEAR_DATA="./data/dataset_for_modeling"Should I pass the folder of the extracted zip file (assuming train.txt is the training dataset) ?

Clearml automatically gets these reported metrics from TB, since you mentioned see the scalars , I assume huggingface reports to TB. Could you verify? Is there a quick code sample to reproduce?

Thanks SmallDeer34 !
This is exactly what I needed

Yeah that should work. Basically in --train_file it needs the path to train.txt, --validation_file needs the path to validation.txt, etc. I just put them all in the same folder for convenience

Reproduce the training:
# How to run `

You need to pip install requirements first. I think the following would do: transformers datasets clearml tokenizers torch

CLEAR_DATA has train.txt and validation.txt, the .txt files just need to have text data on separate lines. For debugging, anything should do.

For training you need tokenizer files as well, vocab.json, merges.txt, and tokenizer.json.

you also need a config.json, should work.

export CLEAR_DATA="./data/dataset_for_modeling"
python3 run_mlm.py
--line_by_line
--seed 420
--config_name "$CLEAR_DATA"
--tokenizer_name "$CLEAR_DATA"
--train_file "$CLEAR_DATA/train.txt"
--validation_file "$CLEAR_DATA/validation.txt"
--max_seq_length 512
--do_train
--do_eval
--evaluation_strategy steps
--eval_steps 500
--save_strategy epoch
--num_train_epochs 3
--per_device_train_batch_size 8
--per_device_eval_batch_size 8
--output_dir ./output/mlm_training_output `Let me get you a dataset