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