Improve nlp.update / training loop overview (see
@@ -149,7 +149,9 @@ p
+aside
| #[+api("language#begin_training") #[code begin_training()]]: Start the
- | training and return an optimizer function to update the model's weights.#[br]
+ | training and return an optimizer function to update the model's weights.
+ | Can take an optional function converting the training data to spaCy's
+ | training format.#[br]
| #[+api("language#update") #[code update()]]: Update the model with the
| training example and gold data.#[br]
| #[+api("language#to_disk") #[code to_disk()]]: Save the updated model to
@@ -165,38 +167,38 @@ p
nlp.update([doc], [gold], drop=0.5, sgd=optimizer)
nlp.to_disk('/model')
-+table(["Name", "Description"])
- +row
- +cell #[code train_data]
- +cell The training data.
-
- +row
- +cell #[code get_data]
- +cell
- | An optional function converting the training data to spaCy's
- | JSON format.
+p
+ | The #[+api("language#update") #[code nlp.update]] method takes the
+ | following arguments:
++table(["Name", "Description"])
+row
- +cell #[code doc]
+ +cell #[code docs]
+cell
| #[+api("doc") #[code Doc]] objects. The #[code update] method
| takes a sequence of them, so you can batch up your training
- | examples.
+ | examples. Alternatively, you can also pass in a sequence of
+ | raw texts.
+row
- +cell #[code gold]
+ +cell #[code golds]
+cell
| #[+api("goldparse") #[code GoldParse]] objects. The #[code update]
| method takes a sequence of them, so you can batch up your
- | training examples.
+ | training examples. Alternatively, you can also pass in a
+ | dictionary containing the annotations.
+row
+cell #[code drop]
- +cell Dropout rate. Makes it harder for the model to just memorise the data.
+ +cell
+ | Dropout rate. Makes it harder for the model to just memorise
+ | the data.
+row
- +cell #[code optimizer]
- +cell Callable to update the model's weights.
+ +cell #[code sgd]
+ +cell
+ | An optimizer, i.e. a callable to update the model's weights. If
+ | not set, spaCy will create a new one and save it for further use.
p
| Instead of writing your own training loop, you can also use the
