Ahmadzei's picture
added 3 more tables for large emb model
5fa1a76
training_args = TrainingArguments(
output_dir="my_awesome_food_model",
remove_unused_columns=False,
evaluation_strategy="epoch",
save_strategy="epoch",
learning_rate=5e-5,
per_device_train_batch_size=16,
gradient_accumulation_steps=4,
per_device_eval_batch_size=16,
num_train_epochs=3,
warmup_ratio=0.1,
logging_steps=10,
load_best_model_at_end=True,
metric_for_best_model="accuracy",
push_to_hub=True,
)
trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=food["train"],
eval_dataset=food["test"],
tokenizer=image_processor,
compute_metrics=compute_metrics,
)
trainer.train()
Once training is completed, share your model to the Hub with the [~transformers.Trainer.push_to_hub] method so everyone can use your model:
trainer.push_to_hub()
If you are unfamiliar with fine-tuning a model with Keras, check out the basic tutorial first!