text: Title: MemoryError while reading a document using NLP.pipe(text, disable=["tagger", "parser"]
  Body: In this use case I am trying to create noun chunks by making use of the spacy, and I am doing this for a batch (Batch size can vary from 20 to 100)
  Need to process 2.8Million documents overall.
  Using Large spacy english model
  In a for loop I am doing NLP on each of the document using NLP.pipe(text, disable=["tagger", "parser"].
  Most of the times it is working fine but for some batches I start getting MemoryError, what is the reason for this error, is the infrastructure issue like insufficient cpu/RAM/Memory while processing that batch or is there a problem with the way I am using spacy in my code
  
  ## How to reproduce the behaviour
  texts = python list of Large documents
  Note: many of these documents have character length of 5 Million , average size of a document is 1.5 million character.
  
  ```
  texts = data_df[CONTENTS].to_list()
  
  with multiprocessing.Pool(processes=no_of_cores) as pool:
      noun_chunks = create_noun_chunks(texts)
  
  def create_noun_chunks(text: Iterable[str]) -> List[Sequence[str]]:
      """
      Create noun chunks for a given text, after remove entities
      :param text: text for which noun chunk is required
      :return: strings, Each noun chunk in the string is delimited by \n
      """
      global NLP
      if NLP is None:
          NLP = spacy.load(settings.NLP_LIB)
          NLP.max_length = 5000000
      all_chunks = []
      for txt, doc in zip(text, NLP.pipe(text, disable=["tagger", "parser"])):
  ```
  
  It is while loading this _for_ loop line that I get the memory error
  
  Is it because each element in the _texts_ is very large text of million of characters and there are 20 or 100 such elements in this list _texts_ that I am running into memory error?
  
  Trace back:
  
  ```
  File "/home/user/projects/my_project/10.6.12/src/core/utilities/text_utility.py", line 174, in create_noun_chunks
      for txt, doc in zip(text, NLP.pipe(text, disable=["tagger", "parser"])):
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/language.py", line 1583, in pipe
      for doc in docs:
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/util.py", line 1611, in _pipe
      yield from proc.pipe(docs, **kwargs)
    File "spacy/pipeline/transition_parser.pyx", line 230, in pipe
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/util.py", line 1560, in minibatch
      batch = list(itertools.islice(items, int(batch_size)))
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/util.py", line 1611, in _pipe
      yield from proc.pipe(docs, **kwargs)
    File "spacy/pipeline/pipe.pyx", line 53, in pipe
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/util.py", line 1611, in _pipe
      yield from proc.pipe(docs, **kwargs)
    File "spacy/pipeline/pipe.pyx", line 53, in pipe
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/util.py", line 1611, in _pipe
      yield from proc.pipe(docs, **kwargs)
    File "spacy/pipeline/trainable_pipe.pyx", line 79, in pipe
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/util.py", line 1630, in raise_error
      raise e
    File "spacy/pipeline/trainable_pipe.pyx", line 75, in spacy.pipeline.trainable_pipe.TrainablePipe.pipe
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/pipeline/tok2vec.py", line 125, in predict
      tokvecs = self.model.predict(docs)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 315, in predict
      return self._func(self, X, is_train=False)[0]
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/chain.py", line 54, in forward
      Y, inc_layer_grad = layer(X, is_train=is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 291, in __call__
      return self._func(self, X, is_train=is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/with_array.py", line 40, in forward
      return _list_forward(cast(Model[List2d, List2d], model), Xseq, is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/with_array.py", line 76, in _list_forward
      Yf, get_dXf = layer(Xf, is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 291, in __call__
      return self._func(self, X, is_train=is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/chain.py", line 54, in forward
      Y, inc_layer_grad = layer(X, is_train=is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 291, in __call__
      return self._func(self, X, is_train=is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/residual.py", line 40, in forward
      Y, backprop_layer = model.layers[0](X, is_train)
    File "/home/`File "/home/user/projects/my_project/10.6.12/src/core/utilities/text_utility.py", line 174, in create_noun_chunks
      for txt, doc in zip(text, NLP.pipe(text, disable=["tagger", "parser"])):
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/language.py", line 1583, in pipe
      for doc in docs:
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/util.py", line 1611, in _pipe
      yield from proc.pipe(docs, **kwargs)
    File "spacy/pipeline/transition_parser.pyx", line 230, in pipe
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/util.py", line 1560, in minibatch
      batch = list(itertools.islice(items, int(batch_size)))
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/util.py", line 1611, in _pipe
      yield from proc.pipe(docs, **kwargs)
    File "spacy/pipeline/pipe.pyx", line 53, in pipe
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/util.py", line 1611, in _pipe
      yield from proc.pipe(docs, **kwargs)
    File "spacy/pipeline/pipe.pyx", line 53, in pipe
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/util.py", line 1611, in _pipe
      yield from proc.pipe(docs, **kwargs)
    File "spacy/pipeline/trainable_pipe.pyx", line 79, in pipe
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/util.py", line 1630, in raise_error
      raise e
    File "spacy/pipeline/trainable_pipe.pyx", line 75, in spacy.pipeline.trainable_pipe.TrainablePipe.pipe
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/spacy/pipeline/tok2vec.py", line 125, in predict
      tokvecs = self.model.predict(docs)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 315, in predict
      return self._func(self, X, is_train=False)[0]
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/chain.py", line 54, in forward
      Y, inc_layer_grad = layer(X, is_train=is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 291, in __call__
      return self._func(self, X, is_train=is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/with_array.py", line 40, in forward
      return _list_forward(cast(Model[List2d, List2d], model), Xseq, is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/with_array.py", line 76, in _list_forward
      Yf, get_dXf = layer(Xf, is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 291, in __call__
      return self._func(self, X, is_train=is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/chain.py", line 54, in forward
      Y, inc_layer_grad = layer(X, is_train=is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 291, in __call__
      return self._func(self, X, is_train=is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/residual.py", line 40, in forward
      Y, backprop_layer = model.layers[0](X, is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 291, in __call__
      return self._func(self, X, is_train=is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/chain.py", line 54, in forward
      Y, inc_layer_grad = layer(X, is_train=is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/ptexts = data_df[CONTENTS].to_list()
  
  with multiprocessing.Pool(processes=no_of_cores) as pool:
      noun_chunks = create_noun_chunks(texts)ython3.7/site-packages/thinc/model.py", line 291, in __call__
      return self._func(self, X, is_train=is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/chain.py", line 54, in forward
      Y, inc_layer_grad = layer(X, is_train=is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 291, in __call__
      return self._func(self, X, is_train=is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/chain.py", line 54, in forward
      Y, inc_layer_grad = layer(X, is_train=is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 291, in __call__
      return self._func(self, X, is_train=is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/maxout.py", line 49, in forward
      Y = model.ops.gemm(X, W, trans2=True)
    File "thinc/backends/numpy_ops.pyx", line 93, in thinc.backends.numpy_ops.NumpyOps.gemm
    File "blis/py.pyx", line 72, in blis.py.gemm
  MemoryErroruser/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 291, in __call__
      return self._func(self, X, is_train=is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/chain.py", line 54, in forward
      Y, inc_layer_grad = layer(X, is_train=is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/ptexts = data_df[CONTENTS].to_list()
  
  with multiprocessing.Pool(processes=no_of_cores) as pool:
      noun_chunks = create_noun_chunks(texts)ython3.7/site-packages/thinc/model.py", line 291, in __call__
      return self._func(self, X, is_train=is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/chain.py", line 54, in forward
      Y, inc_layer_grad = layer(X, is_train=is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 291, in __call__
      return self._func(self, X, is_train=is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/chain.py", line 54, in forward
      Y, inc_layer_grad = layer(X, is_train=is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/model.py", line 291, in __call__
      return self._func(self, X, is_train=is_train)
    File "/home/user/anaconda3/envs/10.6.12/lib/python3.7/site-packages/thinc/layers/maxout.py", line 49, in forward
      Y = model.ops.gemm(X, W, trans2=True)
    File "thinc/backends/numpy_ops.pyx", line 93, in thinc.backends.numpy_ops.NumpyOps.gemm
    File "blis/py.pyx", line 72, in blis.py.gemm
  MemoryError
  ```
  
  
  ## Your Environment
  <!-- Include details of your environment. You can also type `python -m spacy info --markdown` and copy-paste the result here.-->
  * Operating System: RedHat in case of server and Linux machine for development
  * Python Version Used: 3.7.*
  * spaCy Version Used:3.3.*
  * Environment Information: Conda environment
  