patrickvonplaten / rotobart

Pre-training BART in Flax on The Pile dataset

Geek Repo:Geek Repo

Github PK Tool:Github PK Tool

RotoBART

ToDos:

  • Ensure fixed sequence lengths (padding), to avoid jax recompilation
  • Test on our TPU VM
  • Check we're using all TPU capacity
  • Define hyperparamters to train on
  • Model parallel ?
  • Train 128k sentencepiece tokenizer on the Pile
  • add sentence permutation to train script
  • add text infill to train script
  • Add checkpointing

Running the script

Script arguemnts

Available model config arguments from script:

encoder_layers
encoder_ffn_dim
decoder_layers
decoder_ffn_dim
d_model
vocab_size
max_position_embeddings
encoder_layerdrop
decoder_layerdrop

testing : only uses 1 batch, for testing the script adafactor: will enable adafactor, removing the command will revert to Adam grad_accum: what value for gradient accumulation to use, default is 4

python rotobart/run_dnlm_flax.py \
  --output_dir rotobart_output \
  --overwrite_output_dir \
  --dataset_path rotobart/pile.py \
  --model_name_or_path rotobart \
  --tokenizer_name ./rotobart/vocab-2/the_pile.model \
  --shuffle_buffer_size 100_000 \
  --do_train --do_eval \
  --max_seq_length 1024 \
  --encoder_layers 2 \
  --decoder_layers 2 \
  --per_device_train_batch_size 2 \
  --per_device_eval_batch_size 2 \
  --logging_steps 8 \
  --num_train_steps 1000 \
  --eval_steps 1000 \
  --save_steps 1000 \
  --num_eval_samples 100 \
  --warmup_steps 30 \
  --learning_rate 1e-4 \
  --use_wandb \
  --testing \
  --colab_tpu \
  --use_bf16 \
  --adafactor
ezoic increase your site revenue

About

Pre-training BART in Flax on The Pile dataset

License:MIT License


Languages

Language:Python 99.3%Language:Shell 0.7%