(
    d_model=768,
    vocab_size=30522,
    max_len=512,
    type_vocab_size=2,
    n_heads=12,
    d_ff=3072,
    n_layers=12,
    head=None,
    init_checkpoint=None,
    mode='eval',
)
return bert
BERT (default hparams are for bert-base-uncased).
BERT (default hparams are for bert-base-uncased).
BERT ( default hparams are for bert - base - uncased ) .
def BERT(
    d_model=768,
    vocab_size=30522,
    max_len=512,
    type_vocab_size=2,
    n_heads=12,
    d_ff=3072,
    n_layers=12,
    head=None,
    init_checkpoint=None,
    mode='eval',
):
  """BERT (default hparams are for bert-base-uncased)."""
  # TODO(piotrekp1): loading config from model_name

  layer_norm_eps = 1e-12
  d_head = d_model // n_heads

  word_embeddings = tl.Embedding(vocab_size, d_model)
  type_embeddings = tl.Embedding(type_vocab_size, d_model)
  position_embeddings = tl.PositionalEncoding(max_len, mode=mode)
  embeddings = [
      tl.Select([0, 1, 0], n_in=3),  # Drops 'idx' input.
      tl.Parallel(word_embeddings, type_embeddings, [
          tl.PaddingMask(),
          tl.Fn('Squeeze', lambda x: np.squeeze(x, (1, 2)), n_out=1)
      ]),
      tl.Add(),
      position_embeddings,
      tl.LayerNorm(epsilon=layer_norm_eps),
  ]

  encoder = []
  for _ in range(n_layers):
    attn = tl.SelfAttention(
        n_heads=n_heads,
        d_qk=d_head,
        d_v=d_head,
        bias=True,
        masked=True,
        mode=mode)
    feed_forward = [tl.Dense(d_ff), tl.Gelu(), tl.Dense(d_model)]
    encoder += [
        tl.Select([0, 1, 1]),  # Save a copy of the mask
        tl.Residual(attn, AddBias()),  # pylint: disable=no-value-for-parameter
        tl.LayerNorm(epsilon=layer_norm_eps),
        tl.Residual(*feed_forward),
        tl.LayerNorm(epsilon=layer_norm_eps),
    ]

  encoder += [tl.Select([0], n_in=2)]  # Drop the mask

  pooler = [
      tl.Fn('', lambda x: (x[:, 0, :], x), n_out=2),
      tl.Dense(d_model),
      tl.Tanh(),
  ]

  init_checkpoint = init_checkpoint if mode == 'train' else None
  bert = PretrainedBERT(
      embeddings + encoder + pooler, init_checkpoint=init_checkpoint)

  if head is not None:
    bert = tl.Serial(bert, head())

  return bert
def BERT ( d_model = 768 , vocab_size = 30522 , max_len = 512 , type_vocab_size = 2 , n_heads = 12 , d_ff = 3072 , n_layers = 12 , head = None , init_checkpoint = None , mode = 'eval' , ) : # TODO(piotrekp1): loading config from model_name layer_norm_eps = 1e-12 d_head = d_model // n_heads word_embeddings = tl . Embedding ( vocab_size , d_model ) type_embeddings = tl . Embedding ( type_vocab_size , d_model ) position_embeddings = tl . PositionalEncoding ( max_len , mode = mode ) embeddings = [ tl . Select ( [ 0 , 1 , 0 ] , n_in = 3 ) , # Drops 'idx' input. tl . Parallel ( word_embeddings , type_embeddings , [ tl . PaddingMask ( ) , tl . Fn ( 'Squeeze' , lambda x : np . squeeze ( x , ( 1 , 2 ) ) , n_out = 1 ) ] ) , tl . Add ( ) , position_embeddings , tl . LayerNorm ( epsilon = layer_norm_eps ) , ] encoder = [ ] for _ in range ( n_layers ) : attn = tl . SelfAttention ( n_heads = n_heads , d_qk = d_head , d_v = d_head , bias = True , masked = True , mode = mode ) feed_forward = [ tl . Dense ( d_ff ) , tl . Gelu ( ) , tl . Dense ( d_model ) ] encoder += [ tl . Select ( [ 0 , 1 , 1 ] ) , # Save a copy of the mask tl . Residual ( attn , AddBias ( ) ) , # pylint: disable=no-value-for-parameter tl . LayerNorm ( epsilon = layer_norm_eps ) , tl . Residual ( * feed_forward ) , tl . LayerNorm ( epsilon = layer_norm_eps ) , ] encoder += [ tl . Select ( [ 0 ] , n_in = 2 ) ] # Drop the mask pooler = [ tl . Fn ( '' , lambda x : ( x [ : , 0 , : ] , x ) , n_out = 2 ) , tl . Dense ( d_model ) , tl . Tanh ( ) , ] init_checkpoint = init_checkpoint if mode == 'train' else None bert = PretrainedBERT ( embeddings + encoder + pooler , init_checkpoint = init_checkpoint ) if head is not None : bert = tl . Serial ( bert , head ( ) ) return bert