modeling¶
-
class
RoFormerModel
(config: paddlenlp.transformers.roformer.configuration.RoFormerConfig)[source]¶ Bases:
paddlenlp.transformers.roformer.modeling.RoFormerPretrainedModel
The bare RoFormerModel outputting raw hidden-states.
This model inherits from
PretrainedModel
. Refer to the superclass documentation for the generic methods.This model is also a Paddle paddle.nn.Layer subclass. Use it as a regular Paddle Layer and refer to the Paddle documentation for all matter related to general usage and behavior.
- Parameters
config (
RoFormerConfig
) – An instance of RoFormerConfig used to construct RoFormerModel.
-
get_input_embeddings
()[source]¶ get input embedding of model
- Returns
embedding of model
- Return type
nn.Embedding
-
set_input_embeddings
(value)[source]¶ set new input embedding for model
- Parameters
value (Embedding) – the new embedding of model
- Raises
NotImplementedError – Model has not implement
set_input_embeddings
method
-
forward
(input_ids: Optional[paddle.Tensor] = None, token_type_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, inputs_embeds: Optional[paddle.Tensor] = None, past_key_values: Optional[Tuple[Tuple[paddle.Tensor]]] = None, use_cache: Optional[bool] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None)[source]¶ The RoFormerModel forward method, overrides the
__call__()
special method.- Parameters
input_ids (Tensor, optional) – Indices of input sequence tokens in the vocabulary. They are numerical representations of tokens that build the input sequence. It’s data type should be
int64
and has a shape of [batch_size, sequence_length].token_type_ids (Tensor, optional) –
Segment token indices to indicate first and second portions of the inputs. Indices can be either 0 or 1:
0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
It’s data type should be
int64
and has a shape of [batch_size, sequence_length]. Defaults to None, which means no segment embeddings is added to token embeddings.attention_mask (Tensor, optional) – Mask used in multi-head attention to avoid performing attention to some unwanted positions, usually the paddings or the subsequent positions. Its data type can be int, float and bool. When the data type is bool, the
masked
tokens haveFalse
values and the others haveTrue
values. When the data type is int, themasked
tokens have0
values and the others have1
values. When the data type is float, themasked
tokens have-INF
values and the others have0
values. It is a tensor with shape broadcasted to[batch_size, num_attention_heads, sequence_length, sequence_length]
. For example, its shape can be [batch_size, sequence_length], [batch_size, sequence_length, sequence_length], [batch_size, num_attention_heads, sequence_length, sequence_length]. Defaults toNone
, which means nothing needed to be prevented attention to.inputs_embeds (Tensor, optional) – If you want to control how to convert
inputs_ids
indices into associated vectors, you can pass an embedded representation directly instead of passinginputs_ids
.past_key_values (tuple(tuple(Tensor)), optional) – The length of tuple equals to the number of layers, and each inner tuple haves 4 tensors of shape
(batch_size, num_heads, sequence_length - 1, embed_size_per_head)
) which contains precomputed key and value hidden states of the attention blocks. Ifpast_key_values
are used, the user can optionally input only the lastinput_ids
(those that don’t have their past key value states given to this model) of shape(batch_size, 1)
instead of allinput_ids
of shape(batch_size, sequence_length)
.use_cache (
bool
, optional) – If set toTrue
,past_key_values
key value states are returned. Defaults toNone
.output_hidden_states (bool, optional) – Whether to return the hidden states of all layers. Defaults to
False
.output_attentions (bool, optional) – Whether to return the attentions tensors of all attention layers. Defaults to
False
.return_dict (bool, optional) – Whether to return a
ModelOutput
object. IfFalse
, the output will be a tuple of tensors. Defaults toFalse
.
- Returns
An instance of
BaseModelOutputWithPoolingAndCrossAttentions
ifreturn_dict=True
. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields ofBaseModelOutputWithPoolingAndCrossAttentions
.
Example
import paddle from paddlenlp.transformers import RoFormerModel, RoFormerTokenizer tokenizer = RoFormerTokenizer.from_pretrained('roformer-chinese-char-base') model = RoFormerModel.from_pretrained('roformer-chinese-char-base') tokenized_inputs = tokenizer("欢迎使用百度飞桨!", return_tensors="pd") output = model(**tokenized_inputs)
-
class
RoFormerPretrainedModel
(*args, **kwargs)[source]¶ Bases:
paddlenlp.transformers.model_utils.PretrainedModel
An abstract class for pretrained RoFormer models. It provides RoFormer related
model_config_file
,pretrained_init_configuration
,resource_files_names
,pretrained_resource_files_map
,base_model_prefix
for downloading and loading pretrained models. SeePretrainedModel
for more details.-
config_class
¶ alias of
paddlenlp.transformers.roformer.configuration.RoFormerConfig
-
base_model_class
¶ alias of
paddlenlp.transformers.roformer.modeling.RoFormerModel
-
-
class
RoFormerForSequenceClassification
(config: paddlenlp.transformers.roformer.configuration.RoFormerConfig)[source]¶ Bases:
paddlenlp.transformers.roformer.modeling.RoFormerPretrainedModel
RoFormer Model with a linear layer on top of the output layer, designed for sequence classification/regression tasks like GLUE tasks.
- Parameters
config (
RoFormerConfig
) – An instance of RoFormerConfig used to construct RoFormerForSequenceClassification.
-
forward
(input_ids: Optional[paddle.Tensor] = None, token_type_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, inputs_embeds: Optional[paddle.Tensor] = None, labels: Optional[paddle.Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None)[source]¶ The RoFormerForSequenceClassification forward method, overrides the __call__() special method.
- Parameters
input_ids (Tensor) – See
RoFormerModel
.token_type_ids (Tensor, optional) – See
RoFormerModel
.attention_mask (Tensor, optional) – See
RoFormerModel
.inputs_embeds (Tensor, optional) – See
RoFormerModel
.labels (Tensor of shape
(batch_size,)
, optional) – Labels for computing the sequence classification/regression loss. Indices should be in[0, ..., num_classes - 1]
. Ifnum_classes == 1
a regression loss is computed (Mean-Square loss), Ifnum_classes > 1
a classification loss is computed (Cross-Entropy).output_hidden_states (bool, optional) – Whether to return the hidden states of all layers. Defaults to
False
.output_attentions (bool, optional) – Whether to return the attentions tensors of all attention layers. Defaults to
False
.return_dict (bool, optional) – Whether to return a
SequenceClassifierOutput
object. IfFalse
, the output will be a tuple of tensors. Defaults toFalse
.
- Returns
An instance of
SequenceClassifierOutput
ifreturn_dict=True
. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields ofSequenceClassifierOutput
.
Example
import paddle from paddlenlp.transformers import RoFormerForSequenceClassification, RoFormerTokenizer tokenizer = RoFormerTokenizer.from_pretrained('roformer-chinese-char-base') model = RoFormerForSequenceClassification.from_pretrained('roformer-chinese-char-base') tokenized_inputs = tokenizer("欢迎使用百度飞桨!", return_tensors="pd") logits = model(**tokenized_inputs)
-
class
RoFormerForTokenClassification
(config: paddlenlp.transformers.roformer.configuration.RoFormerConfig)[source]¶ Bases:
paddlenlp.transformers.roformer.modeling.RoFormerPretrainedModel
RoFormer Model with a linear layer on top of the hidden-states output layer, designed for token classification tasks like NER tasks.
- Parameters
config (
RoFormerConfig
) – An instance of RoFormerConfig used to construct RoFormerForTokenClassification.
-
forward
(input_ids: Optional[paddle.Tensor] = None, token_type_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, inputs_embeds: Optional[paddle.Tensor] = None, labels: Optional[paddle.Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None)[source]¶ The RoFormerForTokenClassification forward method, overrides the __call__() special method.
- Parameters
input_ids (Tensor) – See
RoFormerModel
.token_type_ids (Tensor, optional) – See
RoFormerModel
.attention_mask (Tensor, optional) – See
RoFormerModel
.inputs_embeds (Tensor, optional) – See
RoFormerModel
.labels (Tensor of shape
(batch_size, sequence_length)
, optional) – Labels for computing the token classification loss. Indices should be in[0, ..., num_classes - 1]
.output_hidden_states (bool, optional) – Whether to return the hidden states of all layers. Defaults to
False
.output_attentions (bool, optional) – Whether to return the attentions tensors of all attention layers. Defaults to
False
.return_dict (bool, optional) – Whether to return a
TokenClassifierOutput
object. IfFalse
, the output will be a tuple of tensors. Defaults toFalse
.
- Returns
An instance of
TokenClassifierOutput
ifreturn_dict=True
. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields ofTokenClassifierOutput
.
Example
import paddle from paddlenlp.transformers import RoFormerForTokenClassification, RoFormerTokenizer tokenizer = RoFormerTokenizer.from_pretrained('roformer-chinese-char-base') model = RoFormerForTokenClassification.from_pretrained('roformer-chinese-char-base') tokenized_inputs = tokenizer("欢迎使用百度飞桨!", return_tensors="pd") logits = model(**tokenized_inputs)
-
class
RoFormerForQuestionAnswering
(config: paddlenlp.transformers.roformer.configuration.RoFormerConfig)[source]¶ Bases:
paddlenlp.transformers.roformer.modeling.RoFormerPretrainedModel
- RoFormer Model with a linear layer on top of the hidden-states output to compute
span_start_logits
and
span_end_logits
, designed for question-answering tasks like SQuAD.
- Parameters
config (
RoFormerConfig
) – An instance of RoFormerConfig used to construct RoFormerForQuestionAnswering.
-
forward
(input_ids: Optional[paddle.Tensor] = None, token_type_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, inputs_embeds: Optional[paddle.Tensor] = None, start_positions: Optional[paddle.Tensor] = None, end_positions: Optional[paddle.Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None)[source]¶ The RoFormerForQuestionAnswering forward method, overrides the __call__() special method.
- Parameters
input_ids (Tensor) – See
RoFormerModel
.token_type_ids (Tensor, optional) – See
RoFormerModel
.attention_mask (Tensor, optional) – See
RoFormerModel
.inputs_embeds (Tensor, optional) – See
RoFormerModel
.start_positions (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 (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.output_hidden_states (bool, optional) – Whether to return the hidden states of all layers. Defaults to
False
.output_attentions (bool, optional) – Whether to return the attentions tensors of all attention layers. Defaults to
False
.return_dict (bool, optional) – Whether to return a
QuestionAnsweringModelOutput
object. IfFalse
, the output will be a tuple of tensors. Defaults toFalse
.
- Returns
An instance of
QuestionAnsweringModelOutput
ifreturn_dict=True
. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields ofQuestionAnsweringModelOutput
.
Example
import paddle from paddlenlp.transformers import RoFormerForQuestionAnswering, RoFormerTokenizer tokenizer = RoFormerTokenizer.from_pretrained('roformer-chinese-char-base') model = RoFormerForQuestionAnswering.from_pretrained('roformer-chinese-char-base') tokenized_inputs = tokenizer("欢迎使用百度飞桨!", return_tensors="pd") outputs = model(**tokenized_inputs)
- RoFormer Model with a linear layer on top of the hidden-states output to compute
-
class
RoFormerForMaskedLM
(config: paddlenlp.transformers.roformer.configuration.RoFormerConfig)[source]¶ Bases:
paddlenlp.transformers.roformer.modeling.RoFormerPretrainedModel
RoFormer Model with a
masked language modeling
head on top.- Parameters
config (
RoFormerConfig
) – An instance of RoFormerConfig used to construct RoFormerForMaskedLM.
-
forward
(input_ids: Optional[paddle.Tensor] = None, token_type_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, inputs_embeds: Optional[paddle.Tensor] = None, labels: Optional[paddle.Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None)[source]¶ The RoFormerForMaskedLM forward method, overrides the __call__() special method.
- Parameters
input_ids (Tensor) – See
RoFormerModel
.token_type_ids (Tensor, optional) – See
RoFormerModel
.attention_mask (Tensor, optional) – See
RoFormerModel
.inputs_embeds (Tensor, optional) – See
RoFormerModel
.labels (Tensor of shape
(batch_size, sequence_length)
, optional) – Labels for computing the masked language modeling loss. Indices should be in[-100, 0, ..., vocab_size]
(seeinput_ids
docstring) Tokens with indices set to-100
are ignored (masked), the loss is only computed for the tokens with labels in[0, ..., vocab_size]
output_hidden_states (bool, optional) – Whether to return the hidden states of all layers. Defaults to
False
.output_attentions (bool, optional) – Whether to return the attentions tensors of all attention layers. Defaults to
False
.return_dict (bool, optional) – Whether to return a
MaskedLMOutput
object. IfFalse
, the output will be a tuple of tensors. Defaults toFalse
.
- Returns
An instance of
MaskedLMOutput
ifreturn_dict=True
. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields ofMaskedLMOutput
.
Example
import paddle from paddlenlp.transformers import RoFormerForMaskedLM, RoFormerTokenizer tokenizer = RoFormerTokenizer.from_pretrained('roformer-chinese-char-base') model = RoFormerForMaskedLM.from_pretrained('roformer-chinese-char-base') tokenized_inputs = tokenizer("欢迎使用百度飞桨!", return_tensors="pd") logits = model(**tokenized_inputs)
-
class
RoFormerForMultipleChoice
(config: paddlenlp.transformers.roformer.configuration.RoFormerConfig)[source]¶ Bases:
paddlenlp.transformers.roformer.modeling.RoFormerPretrainedModel
RoFormerModel with a linear layer on top of the hidden-states output layer, designed for multiple choice tasks like RocStories/SWAG tasks.
- Parameters
config (
RoFormerConfig
) – An instance of RoFormerConfig used to construct RoFormerForMultipleChoice.
-
forward
(input_ids: Optional[paddle.Tensor] = None, token_type_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, inputs_embeds: Optional[paddle.Tensor] = None, labels: Optional[paddle.Tensor] = None, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None)[source]¶ The RoFormerForMultipleChoice forward method, overrides the __call__() special method.
- Parameters
input_ids (Tensor) – See
RoFormerModel
and shape as [batch_size, num_choice, sequence_length].token_type_ids (Tensor, optional) – See
RoFormerModel
and shape as [batch_size, num_choice, sequence_length].attention_mask (Tensor, optional) – See
RoFormerModel
and shape as [batch_size, num_choice, sequence_length].inputs_embeds (Tensor, optional) – See
RoFormerModel
.labels (Tensor of shape
(batch_size, )
, optional) – Labels for computing the multiple choice classification loss. Indices should be in[0, ..., num_choices-1]
wherenum_choices
is the size of the second dimension of the input tensors. (Seeinput_ids
above)output_hidden_states (bool, optional) – Whether to return the hidden states of all layers. Defaults to
False
.output_attentions (bool, optional) – Whether to return the attentions tensors of all attention layers. Defaults to
False
.return_dict (bool, optional) – Whether to return a
MultipleChoiceModelOutput
object. IfFalse
, the output will be a tuple of tensors. Defaults toFalse
.
- Returns
An instance of
MultipleChoiceModelOutput
ifreturn_dict=True
. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields ofMultipleChoiceModelOutput
.
Example
import paddle from paddlenlp.transformers import RoFormerForMultipleChoice, RoFormerTokenizer tokenizer = RoFormerTokenizer.from_pretrained('roformer-chinese-char-base') model = RoFormerForMultipleChoice.from_pretrained('roformer-chinese-char-base') data = [ { "question": "如何打开ipad屏幕?", "answer1": "按音量按钮。", "answer2": "按下锁定按钮。", "label": 1, }, { "question": "如何缩进一些文本?", "answer1": "在开始写之前留一些空格。", "answer2": "按空格键。", "label": 0, }, ] text = [] text_pair = [] for d in data: text.append(d["question"]) text_pair.append(d["answer1"]) text.append(d["question"]) text_pair.append(d["answer2"]) tokenized_inputs = tokenizer(text, text_pair, padding=True, return_tensors="pd") reshaped_logits = model(**tokenized_inputs) print(reshaped_logits.shape) # [2, 2]
-
class
RoFormerForCausalLM
(config: paddlenlp.transformers.roformer.configuration.RoFormerConfig)[source]¶ Bases:
paddlenlp.transformers.roformer.modeling.RoFormerPretrainedModel
RoFormer Model with a
Causal language modeling
head on top.- Parameters
config (
RoFormerConfig
) – An instance of RoFormerConfig used to construct RoFormerForCausalLM.
-
forward
(input_ids: Optional[paddle.Tensor] = None, token_type_ids: Optional[paddle.Tensor] = None, attention_mask: Optional[paddle.Tensor] = None, inputs_embeds: Optional[paddle.Tensor] = None, labels: Optional[paddle.Tensor] = None, past_key_values: Optional[Tuple[Tuple[paddle.Tensor]]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None)[source]¶ The RoFormerForCausalLM forward method, overrides the __call__() special method.
- Parameters
input_ids (Tensor) – See
RoFormerModel
.token_type_ids (Tensor, optional) – See
RoFormerModel
.attention_mask (Tensor, optional) – See
RoFormerModel
.inputs_embeds (Tensor, optional) – See
RoFormerModel
.labels (Tensor of shape
(batch_size, sequence_length)
, optional) – Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in[-100, 0, ..., vocab_size]
(seeinput_ids
docstring) Tokens with indices set to-100
are ignored (masked), the loss is only computed for the tokens with labels in[0, ..., vocab_size]
.past_key_values (tuple(tuple(Tensor)), optional) – See
RoFormerModel
.use_cache (Tensor, optional) – See
RoFormerModel
.output_hidden_states (bool, optional) – Whether to return the hidden states of all layers. Defaults to
False
.output_attentions (bool, optional) – Whether to return the attentions tensors of all attention layers. Defaults to
False
.return_dict (bool, optional) – Whether to return a
CausalLMOutputWithCrossAttentions
object. IfFalse
, the output will be a tuple of tensors. Defaults toFalse
.
- Returns
An instance of
CausalLMOutputWithCrossAttentions
ifreturn_dict=True
. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields ofCausalLMOutputWithCrossAttentions
.
Example
import paddle from paddlenlp.transformers import RoFormerForCausalLM, RoFormerTokenizer tokenizer = RoFormerTokenizer.from_pretrained('roformer-chinese-sim-char-ft-base') model = RoFormerForCausalLM.from_pretrained('roformer-chinese-sim-char-ft-base') tokenized_inputs = tokenizer("欢迎使用百度飞桨!", return_tensors="pd") logits = model(**tokenized_inputs) print(logits.shape) # [1, 11, 12000]