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chore: fix typo in base.py (#5356)

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paramters -> parameters
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Ikko Eltociear Ashimine 2023-07-14 01:40:21 +09:00 committed by GitHub
parent 9891bfeddd
commit 35b2c99f43
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1 changed files with 2 additions and 2 deletions
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haystack/modeling/training/base.py
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@ -667,7 +667,7 @@ class DistillationTrainer(Trainer):
:param disable_tqdm: Disable tqdm progress bar (helps to reduce verbosity in some environments)
:param max_grad_norm: Max gradient norm for clipping, default 1.0, set to None to disable
:param distillation_loss_weight: The weight of the distillation loss. A higher weight means the teacher outputs are more important.
:param distillation_loss: Specifies how teacher and model logits should be compared. Can either be a string ("mse" for mean squared error or "kl_div" for kl divergence loss) or a callable loss function (needs to have named paramters student_logits and teacher_logits)
:param distillation_loss: Specifies how teacher and model logits should be compared. Can either be a string ("mse" for mean squared error or "kl_div" for kl divergence loss) or a callable loss function (needs to have named parameters student_logits and teacher_logits)
:param temperature: The temperature for distillation. A higher temperature will result in less certainty of teacher outputs. A lower temperature means more certainty. A temperature of 1.0 does not change the certainty of the model.
"""
super().__init__(
@ -819,7 +819,7 @@ class TinyBERTDistillationTrainer(Trainer):
:param disable_tqdm: Disable tqdm progress bar (helps to reduce verbosity in some environments)
:param max_grad_norm: Max gradient norm for clipping, default 1.0, set to None to disable
:param distillation_loss_weight: The weight of the distillation loss. A higher weight means the teacher outputs are more important.
:param distillation_loss: Specifies how teacher and model logits should be compared. Can either be a string ("mse" for mean squared error or "kl_div" for kl divergence loss) or a callable loss function (needs to have named paramters student_logits and teacher_logits)
:param distillation_loss: Specifies how teacher and model logits should be compared. Can either be a string ("mse" for mean squared error or "kl_div" for kl divergence loss) or a callable loss function (needs to have named parameters student_logits and teacher_logits)
:param temperature: The temperature for distillation. A higher temperature will result in less certainty of teacher outputs. A lower temperature means more certainty. A temperature of 1.0 does not change the certainty of the model.
"""
super().__init__(