Instructions to use jenspt/byt5_ft_all_clean_data_ws3000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jenspt/byt5_ft_all_clean_data_ws3000 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("jenspt/byt5_ft_all_clean_data_ws3000") model = AutoModelForSeq2SeqLM.from_pretrained("jenspt/byt5_ft_all_clean_data_ws3000") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
training_args = TrainingArguments( output_dir='./results', # output directory num_train_epochs=1, # total number of training epochs per_device_train_batch_size=8, # batch size per device during training #per_device_eval_batch_size=2, # batch size for evaluation warmup_steps=3000, # number of warmup steps for learning rate scheduler (used to be 500) weight_decay=0.01, # strength of weight decay #learning_rate=0.1e-3, # default = 5e-5=0.5e-4 logging_dir='./logs', # directory for storing logs logging_steps=50, #eval_steps = 100, overwrite_output_dir = True, save_strategy = 'epoch', #logging_strategy = 'epoch', )
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