From cc7b62f0901fb0ff7984fc1fa157f572a5885339 Mon Sep 17 00:00:00 2001 From: Benjamin Trom Date: Sun, 5 May 2024 12:39:15 +0200 Subject: [PATCH] apply suggestions from code review --- docs/guides/training_101/index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/guides/training_101/index.md b/docs/guides/training_101/index.md index 64bf80a..0997b6e 100644 --- a/docs/guides/training_101/index.md +++ b/docs/guides/training_101/index.md @@ -375,7 +375,7 @@ class LoggingCallback(Callback[Any]): self.losses = [] ``` -Exactly like models, we need to register the callback to the Trainer. We can do so by adding a CallbackConfig attribute to the Config named `logging` and adding a method to the Trainer class that returns the callback decorated with `@register_callback` decorator. +Exactly like models, we need to register the callback to the Trainer. We can do so by adding a `CallbackConfig` attribute to the config named `logging` and adding a method to the Trainer class that returns the callback decorated with `@register_callback` decorator. ```python from refiners.training_utils import CallbackConfig, register_callback @@ -447,7 +447,7 @@ class EvaluationConfig(CallbackConfig): seed: int ``` -The `TimeValueField` is a custom field that allow Pydantic to parse a string representing a time value (e.g., "50:epochs") into a `TimeValue` object. This is useful to specify the evaluation interval in the configuration file. +The `TimeValueField` is a custom field that allow Pydantic to parse a string representing a time value (e.g., `"50:epochs"`) into a `TimeValue` object. This is useful to specify the evaluation interval in the configuration file. ```python from refiners.training_utils import scoped_seed, Callback