Update Language API docs
@@ -141,10 +141,10 @@ p
p Update the models in the pipeline.
+aside-code("Example").
- with nlp.begin_training(gold, use_gpu=True) as (trainer, optimizer):
- for epoch in trainer.epochs(gold):
- for docs, golds in epoch:
- state = nlp.update(docs, golds, sgd=optimizer)
+ for raw_text, entity_offsets in train_data:
+ doc = nlp.make_doc(raw_text)
+ gold = GoldParse(doc, entities=entity_offsets)
+ nlp.update([doc], [gold], drop=0.5, sgd=optimizer)
+table(["Name", "Type", "Description"])
+row
@@ -173,17 +173,13 @@ p Update the models in the pipeline.
+cell Results from the update.
+h(2, "begin_training") Language.begin_training
- +tag contextmanager
+ +tag method
p
- | Allocate models, pre-process training data and acquire a trainer and
- | optimizer. Used as a contextmanager.
+ | Allocate models, pre-process training data and acquire an optimizer.
+aside-code("Example").
- with nlp.begin_training(gold, use_gpu=True) as (trainer, optimizer):
- for epoch in trainer.epochs(gold):
- for docs, golds in epoch:
- state = nlp.update(docs, golds, sgd=optimizer)
+ optimizer = nlp.begin_training(gold_tuples)
+table(["Name", "Type", "Description"])
+row
@@ -199,7 +195,7 @@ p
+footrow
+cell yields
+cell tuple
- +cell A trainer and an optimizer.
+ +cell An optimizer.
+h(2, "use_params") Language.use_params
+tag contextmanager
