def generate_input(self, device, use_label):
inputs = {}
inputs["task_name"] = self.task_name
inputs["input_ids"] = create_tensor(self.input_features, "input_ids",
torch.long, device)
inputs["input_mask"] = create_tensor(self.input_features, "input_mask",
torch.long, device)
inputs["segment_ids"] = create_tensor(self.input_features,
"segment_ids", torch.long,
device)
inputs["input_head"] = create_tensor(self.input_features, "is_head",
torch.long, device)
if use_label:
label_ids = create_tensor(self.input_features, "label_ids",
torch.long, device)
inputs["label_ids"] = label_ids
else:
inputs["label_ids"] = None
# inputs["extra_args"] = {
# "selected_non_final_layers": [10]
# }
inputs["extra_args"] = {}
if self.config.tasks[
self.task_name]["selected_non_final_layers"] is not None:
inputs["extra_args"][
"selected_non_final_layers"] = self.config.tasks[
self.task_name]["selected_non_final_layers"]
return inputs
def generate_input(self, device, use_label):
"""Generate tensors based on Parallel Feature"""
# BERT based features
inputs = {}
inputs["task_name"] = self.task_name
inputs["a_input_ids"] = create_tensor(self.input_features, "a_input_ids",
torch.long, device)
inputs["b_input_ids"] = create_tensor(self.input_features, "b_input_ids",
torch.long, device)
inputs["a_input_mask"] = create_tensor(self.input_features, "a_input_mask",
torch.long, device)
inputs["b_input_mask"] = create_tensor(self.input_features, "b_input_mask",
torch.long, device)
inputs["a_segment_ids"] = create_tensor(self.input_features, "a_segment_ids",
torch.long, device)
inputs["b_segment_ids"] = create_tensor(self.input_features, "b_segment_ids",
torch.long, device)
inputs["a_selected_indices"] = create_tensor(self.input_features, "a_selected_indices",
torch.long, device)
inputs["b_selected_indices"] = create_tensor(self.input_features, "b_selected_indices",
torch.long, device)
inputs["a_is_head"] = create_tensor(self.input_features, "a_is_head",
torch.long, device)
inputs["b_is_head"] = create_tensor(self.input_features, "b_is_head",
torch.long, device)
inputs["extra_args"] = {}
if self.config.tasks[self.task_name]["selected_non_final_layers"] is not None:
inputs["extra_args"]["selected_non_final_layers"] = self.config.tasks[self.task_name]["selected_non_final_layers"]
return inputs
def generate_input(self, device, use_label):
inputs = {}
inputs["task_name"] = self.task_name
inputs["input_ids"] = create_tensor(self.input_features, "input_ids",
torch.long, device)
inputs["input_mask"] = create_tensor(self.input_features, "input_mask",
torch.long, device)
inputs["segment_ids"] = create_tensor(self.input_features, "segment_ids",
torch.long, device)
inputs["p_input_ids"] = create_tensor(self.input_features, "p_input_ids",
torch.long, device)
inputs["p_input_mask"] = create_tensor(self.input_features, "p_input_mask",
torch.long, device)
inputs["p_segment_ids"] = create_tensor(self.input_features, "p_segment_ids",
torch.long, device)
inputs["n_input_ids"] = create_tensor(self.input_features, "n_input_ids",
torch.long, device)
inputs["n_input_mask"] = create_tensor(self.input_features, "n_input_mask",
torch.long, device)
inputs["n_segment_ids"] = create_tensor(self.input_features, "n_segment_ids",
torch.long, device)
inputs["is_inference"] = not use_label
inputs["extra_args"] = {"target": -torch.ones(inputs["input_ids"].size(0)).to(device)}
return inputs
def generate_input(self, device, use_label):
inputs = {}
inputs["task_name"] = self.task_name
inputs["input_ids"] = create_tensor(self.input_features, "input_ids",
torch.long, device)
inputs["input_mask"] = create_tensor(self.input_features, "input_mask",
torch.long, device)
inputs["segment_ids"] = create_tensor(self.input_features, "segment_ids",
torch.long, device)
inputs["input_head"] = create_tensor(self.input_features, "is_head",
torch.long, device)
if use_label:
label_ids = create_tensor(self.input_features, "label_ids",
torch.long, device)
inputs["label_ids"] = label_ids
else:
inputs["label_ids"] = None
inputs["lang_ids"] = create_tensor(self.input_features, "lang_ids",
torch.long, device)
inputs["extra_args"] = {}
if self.config.tasks[self.task_name]["selected_non_final_layers"] is not None:
inputs["extra_args"]["selected_non_final_layers"] = self.config.tasks[self.task_name]["selected_non_final_layers"]
# Classical Features
inputs["_input_token_ids"] = create_tensor(self.input_features, "_input_token_ids",
torch.long, device)
inputs["_token_length"] = create_tensor(self.input_features, "_token_length",
torch.long, device)
# if inputs["_input_token_ids"] is not None:
# batch_size = inputs["_input_token_ids"].size(0)
# sequence_length = inputs["_input_token_ids"].size(1)
#
# _tmp = torch.arange(0, sequence_length).long().expand(batch_size, sequence_length).to(device)
# inputs["_input_token_mask"] = _tmp < inputs["_token_length"].unsqueeze(1).expand_as(_tmp).contiguous()
# del _tmp
# else:
# inputs["_input_token_mask"] = None
inputs["_input_token_mask"] = create_tensor(self.input_features, "_input_token_mask",
torch.bool, device)
if use_label:
inputs["_label_ids"] = create_tensor(self.input_features, "_label_ids",
torch.long, device)
else:
inputs["_label_ids"] = None
return inputs
def generate_input(self, device, use_label):
"""Generate tensors based on ECP Features"""
# BERT based features
inputs = {}
inputs["task_name"] = self.task_name
inputs["input_ids"] = create_tensor(self.input_features, "input_ids",
torch.long, device)
inputs["input_mask"] = create_tensor(self.input_features, "input_mask",
torch.long, device)
inputs["segment_ids"] = create_tensor(self.input_features,
"segment_ids", torch.long,
device)
inputs["label_ids"] = create_tensor(self.input_features, "label_ids",
torch.long, device)
inputs["clause_candidates"] = create_tensor(self.input_features,
"clause_candidates",
torch.long, device)
inputs["extra_args"] = {}
return inputs
def generate_input(self, device, use_label):
"""Generate tensors based on PointwiseFeatures
"""
# BERT based feature
inputs = {}
inputs["task_name"] = self.task_name
inputs["input_ids"] = create_tensor(self.input_features, "input_ids",
torch.long, device)
inputs["input_mask"] = create_tensor(self.input_features, "input_mask",
torch.long, device)
inputs["segment_ids"] = create_tensor(self.input_features, "segment_ids",
torch.long, device)
inputs["input_head"] = create_tensor(self.input_features, "is_head",
torch.long, device)
if use_label:
inputs["label_ids"] = create_tensor(self.input_features, "label_ids",
torch.long, device)
else:
inputs["label_ids"] = None
inputs["extra_args"] = {}
return inputs
def generate_input(self, device, use_label):
inputs = {}
inputs["task_name"] = self.task_name
inputs["input_ids"] = create_tensor(self.input_features, "input_ids",
torch.long, device)
inputs["input_mask"] = create_tensor(self.input_features, "input_mask",
torch.long, device)
inputs["segment_ids"] = create_tensor(self.input_features,
"segment_ids", torch.long,
device)
inputs["input_head"] = create_tensor(self.input_features, "is_head",
torch.long, device)
if use_label:
if self.config.regression:
inputs["label_ids"] = create_tensor(self.input_features,
"label_ids", torch.float,
device)
else:
inputs["label_ids"] = create_tensor(self.input_features,
"label_ids", torch.long,
device)
else:
inputs["label_ids"] = None
inputs["extra_args"] = {}
return inputs
def generate_input(self, device, use_label):
"""Generate tensors based on PointwiseFeatures
"""
# BERT based feature
inputs = {}
inputs["task_name"] = self.task_name
inputs["input_ids"] = create_tensor(self.input_features, "input_ids",
torch.long, device)
inputs["input_mask"] = create_tensor(self.input_features, "input_mask",
torch.long, device)
inputs["segment_ids"] = create_tensor(self.input_features,
"segment_ids", torch.long,
device)
inputs["input_head"] = create_tensor(self.input_features, "is_head",
torch.long, device)
inputs["text_a_indices"] = create_tensor(self.input_features,
"text_a_indices", torch.long,
device)
inputs["text_b_indices"] = create_tensor(self.input_features,
"text_b_indices", torch.long,
device)
if use_label:
if self.config.regression:
inputs["label_ids"] = create_tensor(self.input_features,
"label_ids", torch.float,
device)
else:
inputs["label_ids"] = create_tensor(self.input_features,
"label_ids", torch.long,
device)
else:
inputs["label_ids"] = None
inputs["extra_args"] = {}
if self.config.tasks[
self.task_name]["selected_non_final_layers"] is not None:
inputs["extra_args"][
"selected_non_final_layers"] = self.config.tasks[
self.task_name]["selected_non_final_layers"]
return inputs
def generate_input(self, device, use_label):
inputs = {}
inputs["task_name"] = self.task_name
inputs["input_ids"] = create_tensor(self.input_features, "input_ids", torch.long, device)
inputs["input_mask"] = create_tensor(self.input_features, "input_mask", torch.long, device)
inputs["segment_ids"] = create_tensor(self.input_features, "segment_ids", torch.long, device)
inputs["input_head"] = create_tensor(self.input_features, "is_head", torch.long, device)
if use_label:
label_ids = create_tensor(self.input_features, "label_ids", torch.long, device)
inputs["label_ids"] = label_ids
arcs_ids = create_tensor(self.input_features, "arcs_ids", torch.long, device)
inputs["arcs_ids"] = arcs_ids
else:
inputs["label_ids"] = None
inputs["arcs_ids"] = None
inputs["lang_ids"] = create_tensor(self.input_features, "lang_ids",
torch.long, device)
inputs["extra_args"] = {}
return inputs
def generate_input(self, device, use_label):
"""Generate tensors based on Parallel Feature"""
# BERT based features
inputs = {}
inputs["task_name"] = self.task_name
inputs["a_input_ids"] = create_tensor(self.input_features,
"a_input_ids", torch.long,
device)
inputs["b_input_ids"] = create_tensor(self.input_features,
"b_input_ids", torch.long,
device)
inputs["a_input_mask"] = create_tensor(self.input_features,
"a_input_mask", torch.long,
device)
inputs["b_input_mask"] = create_tensor(self.input_features,
"b_input_mask", torch.long,
device)
inputs["a_segment_ids"] = create_tensor(self.input_features,
"a_segment_ids", torch.long,
device)
inputs["b_segment_ids"] = create_tensor(self.input_features,
"b_segment_ids", torch.long,
device)
if use_label:
label_ids = create_tensor(self.input_features, "label_ids",
torch.long, device)
inputs["label_ids"] = label_ids
else:
inputs["label_ids"] = None
inputs["extra_args"] = {}
if self.config.tasks[
self.task_name]["selected_non_final_layers"] is not None:
inputs["extra_args"][
"selected_non_final_layers"] = self.config.tasks[
self.task_name]["selected_non_final_layers"]
# Classical Features
inputs["_a_input_token_ids"] = create_tensor(self.input_features,
"_a_input_token_ids",
torch.long, device)
inputs["_b_input_token_ids"] = create_tensor(self.input_features,
"_b_input_token_ids",
torch.long, device)
inputs["_a_token_length"] = create_tensor(self.input_features,
"_a_token_length",
torch.long, device)
inputs["_b_token_length"] = create_tensor(self.input_features,
"_b_token_length",
torch.long, device)
inputs["_a_input_token_mask"] = create_tensor(self.input_features,
"_a_input_token_mask",
torch.long, device)
inputs["_b_input_token_mask"] = create_tensor(self.input_features,
"_b_input_token_mask",
torch.long, device)
if use_label:
_label_ids = create_tensor(self.input_features, "_label_ids",
torch.long, device)
inputs["_label_ids"] = _label_ids
else:
inputs["_label_ids"] = None
return inputs
def generate_input(self, device, use_label):
"""Generate tensors based on SRL Features."""
# BERT based features
inputs = {}
inputs["task_name"] = self.task_name
inputs["input_ids"] = create_tensor(self.input_features, "input_ids",
torch.long, device)
inputs["input_mask"] = create_tensor(self.input_features, "input_mask",
torch.long, device)
inputs["segment_ids"] = create_tensor(self.input_features,
"segment_ids", torch.long,
device)
inputs["input_head"] = create_tensor(self.input_features, "is_head",
torch.long, device)
if use_label:
label_ids = create_tensor(self.input_features, "label_ids",
torch.long, device)
predicate_candidate_label_ids = create_tensor(
self.input_features, "predicate_candidate_label_ids",
torch.long, device)
arg_candidate_label_ids = create_tensor(self.input_features,
"arg_candidate_label_ids",
torch.long, device)
pos_tag_label_ids = create_tensor(self.input_features,
"pos_tag_label_ids", torch.long,
device)
if predicate_candidate_label_ids is None or arg_candidate_label_ids is None:
inputs["label_ids"] = label_ids
else:
inputs["label_ids"] = (label_ids,
predicate_candidate_label_ids,
arg_candidate_label_ids,
pos_tag_label_ids)
else:
inputs["label_ids"] = None
inputs["arg_candidates"] = create_tensor(self.input_features,
"arg_candidates", torch.long,
device)
inputs["predicate_candidates"] = create_tensor(self.input_features,
"predicate_candidates",
torch.long, device)
# Label Embeddings
inputs["label_candidates"] = create_tensor(self.input_features,
"label_candidates",
torch.long, device)
inputs["label_candidates_mask"] = create_tensor(
self.input_features, "label_candidates_mask", torch.long, device)
# Classical features
# inputs["_input_token_ids"] = create_tensor(self.input_features, "_input_token_ids",
# torch.long, device)
# inputs["_token_length"] = create_tensor(self.input_features, "_token_length",
# torch.long, device)
# if use_label:
# _label_ids = create_tensor(self.input_features, "_label_ids",
# torch.long, device)
# _predicate_candidate_label_ids = create_tensor(self.input_features, "_predicate_candidate_label_ids",
# torch.long, device)
# _arg_candidate_label_ids = create_tensor(self.input_features, "_arg_candidate_label_ids",
# torch.long, device)
# if _predicate_candidate_label_ids is None or _arg_candidate_label_ids is None:
# inputs["_label_ids"] = _label_ids
# else:
# inputs["_label_ids"] = (_label_ids, _predicate_candidate_label_ids, _arg_candidate_label_ids)
# else:
# inputs["_label_ids"] = None
# inputs["_arg_candidates"] = create_tensor(
# self.input_features, "_arg_candidates", torch.long, device)
# inputs["_predicate_candidates"] = create_tensor(
# self.input_features, "_predicate_candidates", torch.long, device)
inputs["extra_args"] = {"selected_non_final_layers": [1, 9, 18]}
return inputs
