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Update Tok2Vec.distill docstring

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shadeMe 2023-03-06 10:31:18 +05:30
parent a54efef469
commit c07941d14a
2 changed files with 17 additions and 13 deletions
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14
spacy/pipeline/tok2vec.py
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@ -199,14 +199,14 @@ class Tok2Vec(TrainablePipe):
sgd: Optional[Optimizer] = None,
losses: Optional[Dict[str, float]] = None,
) -> Dict[str, float]:
"""Train a pipe (the student) on the predictions of another pipe
(the teacher). The student is typically trained on the probability
distribution of the teacher, but details may differ per pipe.
"""Performs an update of the student pipe's model using the
student's distillation examples and sets the annotations
of the teacher's distillation examples using the teacher pipe.
teacher_pipe (Optional[TrainablePipe]): The teacher pipe to learn
from.
examples (Iterable[Example]): Distillation examples. The reference
and predicted docs must have the same number of tokens and the
teacher_pipe (Optional[TrainablePipe]): The teacher pipe to use
for prediction.
examples (Iterable[Example]): Distillation examples. The reference (teacher)
and predicted (student) docs must have the same number of tokens and the
same orthography.
drop (float): dropout rate.
sgd (Optional[Optimizer]): An optimizer. Will be created via

16
website/docs/api/tok2vec.mdx
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@ -102,10 +102,14 @@ pipeline components are applied to the `Doc` in order. Both
## Tok2Vec.distill {id="distill", tag="method,experimental", version="4"}
Train a pipe (the student) on the predictions of another pipe (the teacher). The
student is typically trained on the probability distribution of the teacher, but
details may differ per pipe. The goal of distillation is to transfer knowledge
from the teacher to the student.
Performs an update of the student pipe's model using the student's distillation
examples and sets the annotations of the teacher's distillation examples using
the teacher pipe.
Unlike other trainable pipes, the student pipe doesn't directly learn its
representations from the teacher. However, since downstream pipes that do
perform distillation expect the tok2vec annotations to be present on the
correct distillation examples, we need to ensure that they are set beforehand.
The distillation is performed on ~~Example~~ objects. The `Example.reference`
and `Example.predicted` ~~Doc~~s must have the same number of tokens and the
@ -125,8 +129,8 @@ This feature is experimental.
| Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------- |
| `teacher_pipe` | The teacher pipe to learn from. ~~Optional[TrainablePipe]~~ |
| `examples` | Distillation examples. The reference and predicted docs must have the same number of tokens and the same orthography. ~~Iterable[Example]~~ |
| `teacher_pipe` | The teacher pipe to use for prediction. ~~Optional[TrainablePipe]~~ |
| `examples` | Distillation examples. The reference (teacher) and predicted (student) docs must have the same number of tokens and the same orthography. ~~Iterable[Example]~~ |
| _keyword-only_ | |
| `drop` | Dropout rate. ~~float~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |