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1. TASKBOARD 5E UPDATE
2. TASKBOARD 5E FULL

# "epoch": Logging is done at the end of each epoch. # "no": No logging is done during training. # training (used to log training loss for example). # logging_strategy (default: "steps"): The logging strategy to adopt during

TASKBOARD 5E UPDATE

# eval_steps: Number of update steps between two evaluations if

# "epoch": Evaluation is done at the end of each epoch. # "steps": Evaluation is done (and logged) every eval_steps. # "no": No evaluation is done during training. # output_dir: directory where the model checkpoints will be saved. logging_strategy 和 logging_steps 每 50 个训练step保存日志（将由 TensorBoard 可视化）。.Tokenizer = om_pretrained(model_checkpoint, use_fast=True) # original string (character and words) and the token space. # when doing batched tokenization, and additional methods to map between the # The “Fast” implementations allows a significant speed-up in particular # implementation and a “Fast” implementation based on the Rust library Tokenizers.

TASKBOARD 5E FULL

# Most of the tokenizers are available in two flavors: a full python # use_fast: Whether or not to try to load the fast version of the tokenizer. Model_checkpoint = "distilbert-base-uncased" 接下来使用 Hugging Face的AutoTokenizer 类加载 BERT Tokenizer。