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shademe 2023-01-16 17:08:33 +01:00
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@ -100,6 +100,39 @@ pipeline components are applied to the `Doc` in order. Both
| `doc` | The document to process. ~~Doc~~ |
| **RETURNS** | The processed document. ~~Doc~~ |
## 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.
The distillation is performed on ~~Example~~ objects. The `Example.reference`
and `Example.predicted` ~~Doc~~s must have the same number of tokens and the
same orthography. Even though the reference does not need have to have gold
annotations, the teacher could adds its own annotations when necessary.
This feature is experimental.
> #### Example
>
> ```python
> teacher_pipe = teacher.add_pipe("tok2vec")
> student_pipe = student.add_pipe("tok2vec")
> optimizer = nlp.resume_training()
> losses = student.distill(teacher_pipe, examples, sgd=optimizer)
> ```
| 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]~~ |
| _keyword-only_ | |
| `drop` | Dropout rate. ~~float~~ |
| `sgd` | An optimizer. Will be created via [`create_optimizer`](#create_optimizer) if not set. ~~Optional[Optimizer]~~ |
| `losses` | Optional record of the loss during distillation. Updated using the component name as the key. ~~Optional[Dict[str, float]]~~ |
| **RETURNS** | The updated `losses` dictionary. ~~Dict[str, float]~~ |
## Tok2Vec.pipe {id="pipe",tag="method"}
Apply the pipe to a stream of documents. This usually happens under the hood