Fix example [ci skip]
@@ -93,9 +93,6 @@ representations, and then learn a text categorizer on top as a task-specific
TRAIN_DATA = [
("text1", {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}})
]
-DEV_DATA = [
- ("text2", {"cats": {"POSITIVE": 0.0, "NEGATIVE": 1.0}})
-]
```
```python
@@ -122,8 +119,7 @@ for i in range(10):
for batch in minibatch(TRAIN_DATA, size=8):
texts, cats = zip(*batch)
nlp.update(texts, cats, sgd=optimizer, losses=losses)
- scores = nlp.evaluate(DEV_DATA)
- print(i, scores, losses)
+ print(i, losses)
nlp.to_disk("/bert-textcat")
```
