Revert "Fixing reproducible training (#5735)"

This reverts commit c1ea553.

spacy/ml/models/textcat.py

@@ -87,16 +87,16 @@ def build_text_classifier(
  cols = [ORTH, LOWER, PREFIX, SUFFIX, SHAPE, ID]
  with Model.define_operators({">>": chain, "|": concatenate, "**": clone}):
  lower = HashEmbed(
- nO=width, nV=embed_size, column=cols.index(LOWER), dropout=dropout, seed=10
+ nO=width, nV=embed_size, column=cols.index(LOWER), dropout=dropout
  )
  prefix = HashEmbed(
- nO=width // 2, nV=embed_size, column=cols.index(PREFIX), dropout=dropout, seed=11
+ nO=width // 2, nV=embed_size, column=cols.index(PREFIX), dropout=dropout
  )
  suffix = HashEmbed(
- nO=width // 2, nV=embed_size, column=cols.index(SUFFIX), dropout=dropout, seed=12
+ nO=width // 2, nV=embed_size, column=cols.index(SUFFIX), dropout=dropout
  )
  shape = HashEmbed(
- nO=width // 2, nV=embed_size, column=cols.index(SHAPE), dropout=dropout, seed=13
+ nO=width // 2, nV=embed_size, column=cols.index(SHAPE), dropout=dropout
  )
 
  width_nI = sum(layer.get_dim("nO") for layer in [lower, prefix, suffix, shape])

spacy/ml/models/tok2vec.py

@@ -154,16 +154,16 @@ def LayerNormalizedMaxout(width, maxout_pieces):
 def MultiHashEmbed(
  columns, width, rows, use_subwords, pretrained_vectors, mix, dropout
 ):
- norm = HashEmbed(nO=width, nV=rows, column=columns.index("NORM"), dropout=dropout, seed=6)
+ norm = HashEmbed(nO=width, nV=rows, column=columns.index("NORM"), dropout=dropout)
  if use_subwords:
  prefix = HashEmbed(
- nO=width, nV=rows // 2, column=columns.index("PREFIX"), dropout=dropout, seed=7
+ nO=width, nV=rows // 2, column=columns.index("PREFIX"), dropout=dropout
  )
  suffix = HashEmbed(
- nO=width, nV=rows // 2, column=columns.index("SUFFIX"), dropout=dropout, seed=8
+ nO=width, nV=rows // 2, column=columns.index("SUFFIX"), dropout=dropout
  )
  shape = HashEmbed(
- nO=width, nV=rows // 2, column=columns.index("SHAPE"), dropout=dropout, seed=9
+ nO=width, nV=rows // 2, column=columns.index("SHAPE"), dropout=dropout
  )
 
  if pretrained_vectors:
@@ -192,7 +192,7 @@ def MultiHashEmbed(
 
 @registry.architectures.register("spacy.CharacterEmbed.v1")
 def CharacterEmbed(columns, width, rows, nM, nC, features, dropout):
- norm = HashEmbed(nO=width, nV=rows, column=columns.index("NORM"), dropout=dropout, seed=5)
+ norm = HashEmbed(nO=width, nV=rows, column=columns.index("NORM"), dropout=dropout)
  chr_embed = _character_embed.CharacterEmbed(nM=nM, nC=nC)
  with Model.define_operators({">>": chain, "|": concatenate}):
  embed_layer = chr_embed | features >> with_array(norm)