chunked/nltk_chunking/tasks_masked_language_modeling/chunk_49.txt

To finetune a model in TensorFlow, start by setting up an optimizer function, learning rate schedule, and some training hyperparameters:

from transformers import create_optimizer, AdamWeightDecay
optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)

Then you can load DistilRoBERTa with [TFAutoModelForMaskedLM]:

from transformers import TFAutoModelForMaskedLM
model = TFAutoModelForMaskedLM.from_pretrained("distilbert/distilroberta-base")

Convert your datasets to the tf.data.Dataset format with [~transformers.TFPreTrainedModel.prepare_tf_dataset]:

tf_train_set = model.prepare_tf_dataset(
     lm_dataset["train"],
     shuffle=True,
     batch_size=16,
     collate_fn=data_collator,
 )
tf_test_set = model.prepare_tf_dataset(
     lm_dataset["test"],
     shuffle=False,
     batch_size=16,
     collate_fn=data_collator,
 )

Configure the model for training with compile.