fixes
Browse files- modeling_deberta.py +1 -27
modeling_deberta.py
CHANGED
@@ -35,7 +35,7 @@ from transformers.modeling_outputs import (
|
|
35 |
)
|
36 |
from transformers.modeling_utils import PreTrainedModel
|
37 |
from transformers.pytorch_utils import softmax_backward_data
|
38 |
-
from transformers.utils import add_code_sample_docstrings, add_start_docstrings,
|
39 |
from .configuration_deberta import DebertaV2Config
|
40 |
|
41 |
|
@@ -1418,12 +1418,6 @@ class DebertaV2ForSequenceClassification(DebertaV2PreTrainedModel):
|
|
1418 |
def set_input_embeddings(self, new_embeddings):
|
1419 |
self.deberta.set_input_embeddings(new_embeddings)
|
1420 |
|
1421 |
-
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
1422 |
-
@add_code_sample_docstrings(
|
1423 |
-
checkpoint=_CHECKPOINT_FOR_DOC,
|
1424 |
-
output_type=SequenceClassifierOutput,
|
1425 |
-
config_class=_CONFIG_FOR_DOC,
|
1426 |
-
)
|
1427 |
# Copied from transformers.models.deberta.modeling_deberta.DebertaForSequenceClassification.forward with Deberta->DebertaV2
|
1428 |
def forward(
|
1429 |
self,
|
@@ -1517,12 +1511,6 @@ class DebertaV2ForTokenClassification(DebertaV2PreTrainedModel):
|
|
1517 |
# Initialize weights and apply final processing
|
1518 |
self.post_init()
|
1519 |
|
1520 |
-
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
1521 |
-
@add_code_sample_docstrings(
|
1522 |
-
checkpoint=_CHECKPOINT_FOR_DOC,
|
1523 |
-
output_type=TokenClassifierOutput,
|
1524 |
-
config_class=_CONFIG_FOR_DOC,
|
1525 |
-
)
|
1526 |
def forward(
|
1527 |
self,
|
1528 |
input_ids: Optional[torch.Tensor] = None,
|
@@ -1582,14 +1570,6 @@ class DebertaV2ForQuestionAnswering(DebertaV2PreTrainedModel):
|
|
1582 |
# Initialize weights and apply final processing
|
1583 |
self.post_init()
|
1584 |
|
1585 |
-
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
1586 |
-
@add_code_sample_docstrings(
|
1587 |
-
checkpoint=_CHECKPOINT_FOR_DOC,
|
1588 |
-
output_type=QuestionAnsweringModelOutput,
|
1589 |
-
config_class=_CONFIG_FOR_DOC,
|
1590 |
-
qa_target_start_index=_QA_TARGET_START_INDEX,
|
1591 |
-
qa_target_end_index=_QA_TARGET_END_INDEX,
|
1592 |
-
)
|
1593 |
# Copied from transformers.models.deberta.modeling_deberta.DebertaForQuestionAnswering.forward with Deberta->DebertaV2
|
1594 |
def forward(
|
1595 |
self,
|
@@ -1688,12 +1668,6 @@ class DebertaV2ForMultipleChoice(DebertaV2PreTrainedModel):
|
|
1688 |
def set_input_embeddings(self, new_embeddings):
|
1689 |
self.deberta.set_input_embeddings(new_embeddings)
|
1690 |
|
1691 |
-
@add_start_docstrings_to_model_forward(DEBERTA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
|
1692 |
-
@add_code_sample_docstrings(
|
1693 |
-
checkpoint=_CHECKPOINT_FOR_DOC,
|
1694 |
-
output_type=MultipleChoiceModelOutput,
|
1695 |
-
config_class=_CONFIG_FOR_DOC,
|
1696 |
-
)
|
1697 |
def forward(
|
1698 |
self,
|
1699 |
input_ids: Optional[torch.Tensor] = None,
|
|
|
35 |
)
|
36 |
from transformers.modeling_utils import PreTrainedModel
|
37 |
from transformers.pytorch_utils import softmax_backward_data
|
38 |
+
from transformers.utils import add_code_sample_docstrings, add_start_docstrings, logging
|
39 |
from .configuration_deberta import DebertaV2Config
|
40 |
|
41 |
|
|
|
1418 |
def set_input_embeddings(self, new_embeddings):
|
1419 |
self.deberta.set_input_embeddings(new_embeddings)
|
1420 |
|
|
|
|
|
|
|
|
|
|
|
|
|
1421 |
# Copied from transformers.models.deberta.modeling_deberta.DebertaForSequenceClassification.forward with Deberta->DebertaV2
|
1422 |
def forward(
|
1423 |
self,
|
|
|
1511 |
# Initialize weights and apply final processing
|
1512 |
self.post_init()
|
1513 |
|
|
|
|
|
|
|
|
|
|
|
|
|
1514 |
def forward(
|
1515 |
self,
|
1516 |
input_ids: Optional[torch.Tensor] = None,
|
|
|
1570 |
# Initialize weights and apply final processing
|
1571 |
self.post_init()
|
1572 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1573 |
# Copied from transformers.models.deberta.modeling_deberta.DebertaForQuestionAnswering.forward with Deberta->DebertaV2
|
1574 |
def forward(
|
1575 |
self,
|
|
|
1668 |
def set_input_embeddings(self, new_embeddings):
|
1669 |
self.deberta.set_input_embeddings(new_embeddings)
|
1670 |
|
|
|
|
|
|
|
|
|
|
|
|
|
1671 |
def forward(
|
1672 |
self,
|
1673 |
input_ids: Optional[torch.Tensor] = None,
|