lm_fine-tuning on small dataset of 3 documents
## ❓ Questions & Help

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Hi,

I am trying to use [run_lm_finetuning.py](https://github.com/huggingface/transformers/blob/master/examples/run_lm_finetuning.py) on a sample dataset [here](https://github.com/dmlc/gluon-nlp/blob/master/scripts/bert/sample_text.txt).

I am running the script with following arguments but I get the exact identical pytorch_model.bin [440.5 MB] saved in the output_dir=op:
python run_lm_finetuning.py --train_data_file=sample_text.txt --output_dir=op --mlm --do_train --overwrite_output_dir --do_lower_case --save_steps=50

I was wondering if this dataset of 3 documents is too small to fine-tune on or if I can modify some arguments to get a domain-fine-tuned model.

Thanks!
How do you know you have exact identical `pytorch_model.bin` files? Do you just compare file sizes? IF so, it is not a qualified method just because weights usually are just float numbers and they (almost) always occupy same size on the disk. You can compare the hashes of files to make sure.<|||||>Yes, I just thought of comparing the files naively by comparing their sizes. 

I see, yes, "hashes" sounds a much better way of comparing files, thanks. I'll post here if that works.

Also, do you have any beginner suggestions on generating the hashes quickly and efficiently?<|||||>I used md5sum pytorch_model.bin to generate the hashes of the files and both are different. Anyway, thanks, again!