def __init__(self,
params,
tokenizer,
start_mention_id=None,
end_mention_id=None):
super(CrossEncoderModule, self).__init__()
model_path = params["bert_model"]
if params.get("roberta"):
encoder_model = RobertaModel.from_pretrained(model_path)
else:
encoder_model = BertModel.from_pretrained(model_path)
encoder_model.resize_token_embeddings(len(tokenizer))
self.pool_highlighted = params["pool_highlighted"]
self.encoder = BertEncoder(encoder_model,
params["out_dim"],
layer_pulled=params["pull_from_layer"],
add_linear=params["add_linear"]
and not self.pool_highlighted,
get_all_outputs=self.pool_highlighted)
self.config = self.encoder.bert_model.config
self.start_mention_id = start_mention_id
self.end_mention_id = end_mention_id
if self.pool_highlighted:
bert_output_dim = encoder_model.embeddings.word_embeddings.weight.size(
1)
output_dim = params["out_dim"]
self.additional_linear = nn.Linear(2 * bert_output_dim, output_dim)
self.dropout = nn.Dropout(0.1)
def __init__(self, params):
super(BiEncoderModule, self).__init__()
ctxt_bert = BertModel.from_pretrained(params["bert_model"])
cand_bert = BertModel.from_pretrained(params['bert_model'])
self.context_encoder = BertEncoder(
ctxt_bert,
params["out_dim"],
layer_pulled=params["pull_from_layer"],
add_linear=params["add_linear"],
)
self.cand_encoder = BertEncoder(
cand_bert,
params["out_dim"],
layer_pulled=params["pull_from_layer"],
add_linear=params["add_linear"],
)
self.config = ctxt_bert.config
def __init__(self, params):
super(BiEncoderModule, self).__init__()
ctxt_bert = BertModel.from_pretrained(
params["bert_model"]
) # Could be a path containing config.json and pytorch_model.bin; or could be an id shorthand for a model that is loaded in the library
cand_bert = BertModel.from_pretrained(params["bert_model"])
self.context_encoder = BertEncoder(
ctxt_bert,
params["out_dim"],
layer_pulled=params["pull_from_layer"],
add_linear=params["add_linear"],
)
self.cand_encoder = BertEncoder(
cand_bert,
params["out_dim"],
layer_pulled=params["pull_from_layer"],
add_linear=params["add_linear"],
)
self.config = ctxt_bert.config
def __init__(self, params, tokenizer):
super(CrossEncoderModule, self).__init__()
model_path = params["bert_model"]
if params.get("roberta"):
encoder_model = RobertaModel.from_pretrained(model_path)
else:
encoder_model = BertModel.from_pretrained(model_path)
encoder_model.resize_token_embeddings(len(tokenizer))
self.encoder = BertEncoder(
encoder_model,
params["out_dim"],
layer_pulled=params["pull_from_layer"],
add_linear=params["add_linear"],
)
self.config = self.encoder.bert_model.config