  spacy.pipeline, and predictions are available via `doc.cats`. For more details,
  see the documentation:
  * Training: https://spacy.io/usage/training
  
  Compatible with: spaCy v2.0.0+
  """
  from __future__ import unicode_literals, print_function
  
  import ml_datasets
  import plac
 
</s> remove automatically via Thinc's built-in dataset loader. The model is added to
 </s> add automatically via the package `ml_datasets`. The model is added to </s> remove import ml_datasets
 </s> add  </s> remove from spacy.ml._layers import PrecomputableAffine
from spacy.ml._layers import _backprop_precomputable_affine_padding
 </s> add from spacy.ml._precomputable_affine import PrecomputableAffine, _backprop_precomputable_affine_padding </s> remove 
def get_batch(batch_size):
    vocab = Vocab()
    docs = []
    start = 0
    for size in range(1, batch_size + 1):
        # Make the words numbers, so that they're distinct
        # across the batch, and easy to track.
        numbers = [str(i) for i in range(start, start + size)]
        docs.append(Doc(vocab, words=numbers))
        start += size
    return docs
 </s> add from .util import get_batch
Compatible with: spaCy v3.0.0+
  spacy.pipeline, and predictions are available via `doc.cats`. For more details,
  see the documentation:
  * Training: https://spacy.io/usage/training
  
Compatible with: spaCy v3.0.0+
  """
  from __future__ import unicode_literals, print_function
  
  import ml_datasets
  import plac
keep keep keep keep replace keep keep keep keep keep
 Train textcat with config (#5143)

* bring back default build_text_classifier method

* remove _set_dims_ hack in favor of proper dim inference

* add tok2vec initialize to unit test

* small fixes

* add unit test for various textcat config settings

* logistic output layer does not have nO

* fix window_size setting

* proper fix

* fix W initialization

* Update textcat training example

* Use ml_datasets
* Convert training data to `Example` format
* Use `n_texts` to set proportionate dev size

* fix _init renaming on latest thinc

* avoid setting a non-existing dim

* update to thinc==8.0.0a2

* add BOW and CNN defaults for easy testing

* various experiments with train_textcat script, fix softmax activation in textcat bow

* allow textcat train script to work on other datasets as well

* have dataset as a parameter

* train textcat from config, with example config

* add config for training textcat

* formatting

* fix exclusive_classes

* fixing BOW for GPU

* bump thinc to 8.0.0a3 (not published yet so CI will fail)

* add in link_vectors_to_models which got deleted

Co-authored-by: Adriane Boyd <adrianeboyd@gmail.com> 
examples/training/train_textcat.py