Finegrain/refiners
1
0
Fork 0
mirror of https://github.com/finegrain-ai/refiners.git synced 2024-09-19 19:05:28 +00:00
Code Issues Actions 9 Packages Projects Releases Wiki Activity

apply suggestions from code review

Browse source
This commit is contained in:
Benjamin Trom 2024-05-05 12:39:15 +02:00
parent 76a6ce8641
commit cc7b62f090
1 changed files with 2 additions and 2 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

4
docs/guides/training_101/index.md
View file

@ -375,7 +375,7 @@ class LoggingCallback(Callback[Any]):
self.losses = [] 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 ```python
from refiners.training_utils import CallbackConfig, register_callback from refiners.training_utils import CallbackConfig, register_callback
@ -447,7 +447,7 @@ class EvaluationConfig(CallbackConfig):
seed: int 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 ```python
from refiners.training_utils import scoped_seed, Callback from refiners.training_utils import scoped_seed, Callback