modeling#
- class FunnelModel(config: FunnelConfig)[源代码]#
基类:
FunnelPreTrainedModel
- set_input_embeddings(new_embeddings)[源代码]#
set new input embedding for model
- 参数:
value (Embedding) -- the new embedding of model
- 抛出:
NotImplementedError -- Model has not implement
set_input_embeddings
method
- forward(input_ids=None, attention_mask=None, token_type_ids=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None)[源代码]#
Defines the computation performed at every call. Should be overridden by all subclasses.
- 参数:
*inputs (tuple) -- unpacked tuple arguments
**kwargs (dict) -- unpacked dict arguments
- class FunnelForSequenceClassification(config, num_classes=2)[源代码]#
基类:
FunnelPreTrainedModel
- base_model_class#
FunnelModel
的别名
- forward(input_ids=None, attention_mask=None, token_type_ids=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None)[源代码]#
- labels (
paddle.Tensor
of shape(batch_size,)
,optional
): Labels for computing the sequence classification/regression loss. Indices should be in
[0, ..., config.num_labels - 1]
. Ifconfig.num_labels == 1
a regression loss is computed (Mean-Square loss), Ifconfig.num_labels > 1
a classification loss is computed (Cross-Entropy).
- labels (
- class FunnelForTokenClassification(config, num_classes=2)[源代码]#
基类:
FunnelPreTrainedModel
- forward(input_ids=None, attention_mask=None, token_type_ids=None, inputs_embeds=None, labels=None, output_attentions=None, output_hidden_states=None, return_dict=None)[源代码]#
- labels (
paddle.Tensor
of shape(batch_size, sequence_length)
,optional
): Labels for computing the token classification loss. Indices should be in
[0, ..., config.num_labels - 1]
.
- labels (
- class FunnelForQuestionAnswering(config)[源代码]#
基类:
FunnelPreTrainedModel
- forward(input_ids=None, attention_mask=None, token_type_ids=None, inputs_embeds=None, start_positions=None, end_positions=None, output_attentions=None, output_hidden_states=None, return_dict=None)[源代码]#
- start_positions (
paddle.Tensor
of shape(batch_size,)
,optional
): Labels for position (index) of the start of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (
sequence_length
). Position outside of the sequence are not taken into account for computing the loss.- end_positions (
paddle.Tensor
of shape(batch_size,)
,optional
): Labels for position (index) of the end of the labelled span for computing the token classification loss. Positions are clamped to the length of the sequence (
sequence_length
). Position outside of the sequence are not taken into account for computing the loss.
- start_positions (