chunked/nltk_chunking/tasks_question_answering/chunk_31.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
batch_size = 16
num_epochs = 2
total_train_steps = (len(tokenized_squad["train"]) // batch_size) * num_epochs
optimizer, schedule = create_optimizer(
     init_lr=2e-5,
     num_warmup_steps=0,
     num_train_steps=total_train_steps,
 )

Then you can load DistilBERT with [TFAutoModelForQuestionAnswering]:

from transformers import TFAutoModelForQuestionAnswering
model = TFAutoModelForQuestionAnswering.from_pretrained("distilbert/distilbert-base-uncased")

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

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

Configure the model for training with compile:

import tensorflow as tf
model.compile(optimizer=optimizer)

The last thing to setup before you start training is to provide a way to push your model to the Hub.