Ahmadzei's picture
added 3 more tables for large emb model
5fa1a76
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 T5 with [TFAutoModelForSeq2SeqLM]:
from transformers import TFAutoModelForSeq2SeqLM
model = TFAutoModelForSeq2SeqLM.from_pretrained(checkpoint)
Convert your datasets to the tf.data.Dataset format with [~transformers.TFPreTrainedModel.prepare_tf_dataset]:
tf_train_set = model.prepare_tf_dataset(
tokenized_billsum["train"],
shuffle=True,
batch_size=16,
collate_fn=data_collator,
)
tf_test_set = model.prepare_tf_dataset(
tokenized_billsum["test"],
shuffle=False,
batch_size=16,
collate_fn=data_collator,
)
Configure the model for training with compile.