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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, sgd: Optional[Optimizer] = None,
losses: Optional[Dict[str, float]] = None, losses: Optional[Dict[str, float]] = None,
) -> Dict[str, float]: ) -> Dict[str, float]:
"""Train a pipe (the student) on the predictions of another pipe """Performs an update of the student pipe's model using the
(the teacher). The student is typically trained on the probability student's distillation examples and sets the annotations
distribution of the teacher, but details may differ per pipe. of the teacher's distillation examples using the teacher pipe.
teacher_pipe (Optional[TrainablePipe]): The teacher pipe to learn teacher_pipe (Optional[TrainablePipe]): The teacher pipe to use
from. for prediction.
examples (Iterable[Example]): Distillation examples. The reference examples (Iterable[Example]): Distillation examples. The reference (teacher)
and predicted docs must have the same number of tokens and the and predicted (student) docs must have the same number of tokens and the
same orthography. same orthography.
drop (float): dropout rate. drop (float): dropout rate.
sgd (Optional[Optimizer]): An optimizer. Will be created via 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"} ## Tok2Vec.distill {id="distill", tag="method,experimental", version="4"}
Train a pipe (the student) on the predictions of another pipe (the teacher). The Performs an update of the student pipe's model using the student's distillation
student is typically trained on the probability distribution of the teacher, but examples and sets the annotations of the teacher's distillation examples using
details may differ per pipe. The goal of distillation is to transfer knowledge the teacher pipe.
from the teacher to the student.
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` The distillation is performed on ~~Example~~ objects. The `Example.reference`
and `Example.predicted` ~~Doc~~s must have the same number of tokens and the and `Example.predicted` ~~Doc~~s must have the same number of tokens and the
@ -125,8 +129,8 @@ This feature is experimental.
| Name | Description | | Name | Description |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------- | | -------------- | ------------------------------------------------------------------------------------------------------------------------------------------- |
| `teacher_pipe` | The teacher pipe to learn from. ~~Optional[TrainablePipe]~~ | | `teacher_pipe` | The teacher pipe to use for prediction. ~~Optional[TrainablePipe]~~ |
| `examples` | Distillation examples. The reference and predicted docs must have the same number of tokens and the same orthography. ~~Iterable[Example]~~ | | `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_ | | | _keyword-only_ | |
| `drop` | Dropout rate. ~~float~~ | | `drop` | Dropout rate. ~~float~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ | | `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |