Instead, we'll | |
only look at the loss: | |
thon | |
from transformers import Seq2SeqTrainingArguments | |
training_args = Seq2SeqTrainingArguments( | |
output_dir="speecht5_finetuned_voxpopuli_nl", # change to a repo name of your choice | |
per_device_train_batch_size=4, | |
gradient_accumulation_steps=8, | |
learning_rate=1e-5, | |
warmup_steps=500, | |
max_steps=4000, | |
gradient_checkpointing=True, | |
fp16=True, | |
evaluation_strategy="steps", | |
per_device_eval_batch_size=2, | |
save_steps=1000, | |
eval_steps=1000, | |
logging_steps=25, | |
report_to=["tensorboard"], | |
load_best_model_at_end=True, | |
greater_is_better=False, | |
label_names=["labels"], | |
push_to_hub=True, | |
) | |
Instantiate the Trainer object and pass the model, dataset, and data collator to it. |