id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
schriner-data-2023	schriner	schriner-data-2023	2023		.html	application/xhtml+xml	3853	193	60	We can now start the training: fairseq-train \ --data-bin \ --source-lang epo.g \ --target-lang epo.p \ --encoder-bidirectional \ --seed {choose a random whole numeral} \ --arch lstm \ --dropout 0.2 \ --lr .001 \ --max-update 800 \ --no-epoch-checkpoints \ --batch-size 3000 \ --clip-norm 1 \ --label-smoothing .1 \ --optimizer adam \ --clip-norm 1 \ --criterion label_smoothed_cross_entropy \ --encoder-embed-dim 128 \ --decoder-embed-dim 128 \ --encoder-layers 1 \ --decoder-layers 1   With these parameters it took my machine[15] a half-hour to train.  i t a ʒ o     The TSV is shuffled using shuf and then split into three TSV files: an 80% training set, a 10% development set, and a 10% test set using a Python script.[14] python3 split.py \ --seed 103 \ --input_path epo.tsv \ --train_path epo_train.tsv \ --dev_path epo_dev.tsv \ --test_path epo_train.tsv   To prepare the data for Fairseq, the important part of the code to note is that each of the three TSV files are then split into .g (for grapheme) and .p (for phoneme) files for training, dev, and test:	cache/schriner-data-2023.html	txt/schriner-data-2023.txt
