def __init__(self, **kwargs):
super().__init__()
# You dont actually have to pass this if you are using the `load_bert_vocab` call from your
# tokenizer. In this case, a singleton variable will contain the vocab and it will be returned
# by `load_bert_vocab`
# If you trained your model with MEAD/Baseline, you will have a `*.json` file which would want to
# reference here
vocab_file = kwargs.get('vocab_file')
if vocab_file and os.path.exists(vocab_file):
if vocab_file.endswith('.json'):
self.vocab = read_config_stream(kwargs.get('vocab_file'))
else:
self.vocab = load_bert_vocab(kwargs.get('vocab_file'))
else:
self.vocab = kwargs.get('vocab', kwargs.get('known_vocab'))
if self.vocab is None or isinstance(self.vocab,
collections.Counter):
self.vocab = load_bert_vocab(None)
# When we reload, allows skipping restoration of these embeddings
# If the embedding wasnt trained with token types, this allows us to add them later
self.skippable = set(listify(kwargs.get('skip_restore_embeddings',
[])))
self.cls_index = self.vocab.get('[CLS]', self.vocab.get('<s>'))
self.vsz = max(self.vocab.values()) + 1
self.d_model = int(kwargs.get('dsz', kwargs.get('d_model', 768)))
self.init_embed(**kwargs)
self.proj_to_dsz = pytorch_linear(
self.dsz, self.d_model) if self.dsz != self.d_model else _identity
self.init_transformer(**kwargs)
self.return_mask = kwargs.get('return_mask', False)
def __init__(self, name=None, **kwargs):
super().__init__(name=name)
# You dont actually have to pass this if you are using the `load_bert_vocab` call from your
# tokenizer. In this case, a singleton variable will contain the vocab and it will be returned
# by `load_bert_vocab`
# If you trained your model with MEAD/Baseline, you will have a `*.json` file which would want to
# reference here
vocab_file = kwargs.get('vocab_file')
if vocab_file and vocab_file.endswith('.json'):
self.vocab = read_json(vocab_file)
else:
self.vocab = load_bert_vocab(kwargs.get('vocab_file'))
# When we reload, allows skipping restoration of these embeddings
# If the embedding wasnt trained with token types, this allows us to add them later
self.skippable = set(listify(kwargs.get('skip_restore_embeddings',
[])))
self.cls_index = self.vocab.get('[CLS]', self.vocab.get('<s>'))
self.vsz = max(self.vocab.values()) + 1
self.d_model = int(kwargs.get('dsz', kwargs.get('d_model', 768)))
self.init_embed(**kwargs)
self.proj_to_dsz = tf.keras.layers.Dense(
self.dsz, self.d_model) if self.dsz != self.d_model else _identity
self.init_transformer(**kwargs)
def __init__(self, name, **kwargs):
super().__init__(name=name, **kwargs)
self.dsz = kwargs.get('dsz')
self.model = BertModel.from_pretrained(kwargs.get('handle'))
self.vocab = load_bert_vocab(None)
self.vsz = len(
self.vocab
) # 30522 self.model.embeddings.word_embeddings.num_embeddings
def __init__(self, name, **kwargs):
super().__init__(name=name, **kwargs)
self.handle = BERTHubModel.TF_HUB_URL + kwargs.get('embed_file')
if 'vocab' in kwargs:
self.vocab = kwargs['vocab']
else:
self.vocab = load_bert_vocab(kwargs.get('vocab_file'))
self.vsz = len(self.vocab)
self.dsz = kwargs.get('dsz')
self.trainable = kwargs.get('trainable', False)
def __init__(self, name, **kwargs):
super().__init__(name=name)
self.dsz = kwargs.get('dsz')
self.bert_config = BertConfig.from_json_file(kwargs.get('bert_config'))
self.vocab = load_bert_vocab(kwargs.get('vocab_file'))
self.vsz = self.bert_config.vocab_size
assert self.vsz == len(self.vocab)
self.use_one_hot_embeddings = kwargs.get('use_one_hot_embeddings', False)
self.layer_indices = kwargs.get('layers', [-1, -2, -3, -4])
self.operator = kwargs.get('operator', 'concat')
def __init__(self, **kwargs):
super().__init__()
# You dont actually have to pass this if you are using the `load_bert_vocab` call from your
# tokenizer. In this case, a singleton variable will contain the vocab and it will be returned
# by `load_bert_vocab`
# If you trained your model with MEAD/Baseline, you will have a `*.json` file which would want to
# reference here
vocab_file = kwargs.get('vocab_file')
if vocab_file and vocab_file.endswith('.json'):
self.vocab = read_config_stream(kwargs.get('vocab_file'))
else:
self.vocab = load_bert_vocab(kwargs.get('vocab_file'))
self.cls_index = self.vocab['[CLS]']
self.vsz = max(self.vocab.values()) + 1
self.d_model = int(kwargs.get('dsz', kwargs.get('d_model', 768)))
self.init_embed(**kwargs)
self.proj_to_dsz = pytorch_linear(
self.dsz, self.d_model) if self.dsz != self.d_model else _identity
self.init_transformer(**kwargs)
