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 have False values and the others have True values. When the data type is int, the masked tokens have 0 values and the others have 1 values. When the data type is float, the masked tokens have -INF values and the others have 0 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 to None, 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 passing inputs_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. If past_key_values are used, the user can optionally input only the last input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, 1) instead of all input_ids of shape (batch_size, sequence_length).
use_cache (bool, optional) – If set to True, past_key_values key value states are returned. Defaults to None.
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. If False, the output will be a tuple of tensors. Defaults to False.

Returns

An instance of BaseModelOutputWithPoolingAndCrossAttentions if return_dict=True. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of BaseModelOutputWithPoolingAndCrossAttentions.

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. See PretrainedModel for more details.

config_class¶: alias of paddlenlp.transformers.roformer.configuration.RoFormerConfig

init_weights(layer)[source]¶: Initialization hook

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]. If num_classes == 1 a regression loss is computed (Mean-Square loss), If num_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. If False, the output will be a tuple of tensors. Defaults to False.

Returns

An instance of SequenceClassifierOutput if return_dict=True. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of SequenceClassifierOutput.

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. If False, the output will be a tuple of tensors. Defaults to False.

Returns

An instance of TokenClassifierOutput if return_dict=True. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of TokenClassifierOutput.

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. If False, the output will be a tuple of tensors. Defaults to False.

Returns

An instance of QuestionAnsweringModelOutput if return_dict=True. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of QuestionAnsweringModelOutput.

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)

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] (see input_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. If False, the output will be a tuple of tensors. Defaults to False.

Returns

An instance of MaskedLMOutput if return_dict=True. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of MaskedLMOutput.

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] where num_choices is the size of the second dimension of the input tensors. (See input_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. If False, the output will be a tuple of tensors. Defaults to False.

Returns

An instance of MultipleChoiceModelOutput if return_dict=True. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of MultipleChoiceModelOutput.

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] (see input_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. If False, the output will be a tuple of tensors. Defaults to False.

Returns

An instance of CausalLMOutputWithCrossAttentions if return_dict=True. Otherwise it returns a tuple of tensors corresponding to ordered and not None (depending on the input arguments) fields of CausalLMOutputWithCrossAttentions.

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]