modeling#
- class RoFormerv2Model(config: RoFormerv2Config)[源代码]#
-
The bare RoFormerv2 Model transformer 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.
- 参数:
vocab_size (int) -- Vocabulary size of
inputs_ids
inRoFormerv2Model
. Also is the vocab size of token embedding matrix. Defines the number of different tokens that can be represented by theinputs_ids
passed when callingRoFormerv2Model
.hidden_size (int, optional) -- Dimensionality of the, encoder layers and pooler layer. Defaults to
768
.num_hidden_layers (int, optional) -- Number of hidden layers in the Transformer encoder. Defaults to
12
.num_attention_heads (int, optional) -- Number of attention heads for each attention layer in the Transformer encoder. Defaults to
12
.intermediate_size (int, optional) -- Dimensionality of the feed-forward (ff) layer in the encoder. Input tensors to ff layers are firstly projected from
hidden_size
tointermediate_size
, and then projected back tohidden_size
. Typicallyintermediate_size
is larger thanhidden_size
. Defaults to3072
.hidden_act (str, optional) -- The non-linear activation function in the feed-forward layer.
"gelu"
,"relu"
and any other paddle supported activation functions are supported. Defaults to"relu"
.hidden_dropout_prob (float, optional) -- The dropout probability for all fully connected layers in the embeddings and encoder. Defaults to
0.1
.attention_probs_dropout_prob (float, optional) -- The dropout probability used in MultiHeadAttention in all encoder layers to drop some attention target. Defaults to
0.1
.max_position_embeddings (int, optional) -- The maximum value of the dimensionality of position encoding, which dictates the maximum supported length of an input sequence. Defaults to
512
.type_vocab_size (int, optional) -- The vocabulary size of
token_type_ids
. Defaults to2
.pad_token_id (int, optional) -- The index of padding token in the token vocabulary. Defaults to
0
.rotary_value (
bool
, optional) -- Whether or not apply rotay position embeddings to value. Defaults toFalse
.use_bias (
bool
, optional) -- Whether or not use bias. Defaults toFalse
.
- forward(input_ids, token_type_ids=None, attention_mask=None, output_hidden_states=False)[源代码]#
The RoFormerv2Model forward method, overrides the
__call__()
special method.- 参数:
input_ids (Tensor) -- Indices of input sequence tokens in the vocabulary. They are numerical representations of tokens that build the input sequence. Its data type should be
int64
and it has a shape of [batch_size, sequence_length].token_type_ids (Tensor, optional) --
Segment token indices to indicate different portions of the inputs. Selected in the range
[0, type_vocab_size - 1]
. Iftype_vocab_size
is 2, which means the inputs have two portions. Indices can either be 0 or 1:0 corresponds to a sentence A token,
1 corresponds to a sentence B token.
Its data type should be
int64
and it has a shape of [batch_size, sequence_length]. Defaults toNone
, which means we don't add segment embeddings.attention_mask (Tensor, optional) -- Mask used in multi-head attention to avoid performing attention on 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 have0.0
values and the others have1.0
values. It is a tensor with shape broadcasted to[batch_size, num_attention_heads, sequence_length, sequence_length]
. Currently, we only support 2D attention_mask. Defaults toNone
, which meanspad_token_id
will be ignored.output_hidden_states (bool, optional) -- Whether to return the output of each hidden layers. Defaults to
False
.
- 返回:
Returns
sequence_output
orencoder_outputs
.With the fields:
sequence_output
(Tensor):Sequence of hidden-states at the last layer of the model. It's data type should be float32 and its shape is [batch_size, sequence_length, hidden_size].
encoder_outputs
(List(Tensor)):A list of Tensor containing hidden-states of the model at each hidden layer in the Transformer encoder. The length of the list is
num_hidden_layers
. Each Tensor has a data type of float32 and its shape is [batch_size, sequence_length, hidden_size].
- 返回类型:
tuple
示例
import paddle from paddlenlp.transformers import RoFormerv2Model, RoFormerv2Tokenizer tokenizer = RoFormerv2Tokenizer.from_pretrained('roformer_v2_chinese_char_base') model = RoFormerv2Model.from_pretrained('roformer_v2_chinese_char_base') inputs = tokenizer("欢迎使用百度飞桨!") inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()} output = model(**inputs)
- get_input_embeddings() Embedding [源代码]#
get input embedding of model
- 返回:
embedding of model
- 返回类型:
nn.Embedding
- set_input_embeddings(embedding: Embedding)[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
- class RoFormerv2ForMaskedLM(config: RoFormerv2Config)[源代码]#
-
RoFormerv2 Model with a
masked language modeling
head on top.- 参数:
roformerv2 (
RoFormerv2Model
) -- An instance ofRoFormerv2Model
.
- forward(input_ids, token_type_ids=None, attention_mask=None)[源代码]#
- 参数:
input_ids (Tensor) -- See
RoFormerv2Model
.token_type_ids (Tensor, optional) -- See
RoFormerv2Model
.attention_mask (Tensor, optional) -- See
RoFormerv2Model
.
- 返回:
Returns tensor
prediction_scores
, The scores of masked token prediction. Its data type should be float32 and shape is [batch_size, sequence_length, vocab_size].- 返回类型:
Tensor
示例
import paddle from paddlenlp.transformers import RoFormerv2ForMaskedLM, RoFormerv2Tokenizer tokenizer = RoFormerv2Tokenizer.from_pretrained('roformer_v2_chinese_char_base') model = RoFormerv2ForMaskedLM.from_pretrained('roformer_v2_chinese_char_base') inputs = tokenizer("欢迎使用百度飞桨!") inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()} logits = model(**inputs) print(logits.shape) # [1, 11, 12000]
- class RoFormerv2PretrainedModel(*args, **kwargs)[源代码]#
-
An abstract class for pretrained RoFormerv2 models. It provides RoFormerv2 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#
RoFormerv2Config
的别名
- base_model_class#
RoFormerv2Model
的别名
- class RoFormerv2ForSequenceClassification(config: RoFormerv2Config)[源代码]#
-
RoFormerv2 Model with a linear layer on top of the output layer, designed for sequence classification/regression tasks like GLUE tasks.
- 参数:
roformerv2 (
RoFormerv2Model
) -- An instance ofpaddlenlp.transformers.RoFormerv2Model
.num_labels (int, optional) -- The number of classes. Default to
2
.dropout (float, optional) -- The dropout probability for output of RoFormerv2. If None, use the same value as
hidden_dropout_prob
ofpaddlenlp.transformers.RoFormerv2Model
instance. Defaults toNone
.
- forward(input_ids, token_type_ids=None, attention_mask=None)[源代码]#
- 参数:
input_ids (Tensor) -- See
RoFormerv2Model
.token_type_ids (Tensor, optional) -- See
RoFormerv2Model
.attention_mask (Tensor, optional) -- See
RoFormerv2Model
.
- 返回:
Returns tensor
logits
, a tensor of the input text classification logits. Shape as[batch_size, num_labels]
and dtype as float32.- 返回类型:
Tensor
示例
import paddle from paddlenlp.transformers import RoFormerv2ForSequenceClassification, RoFormerv2Tokenizer tokenizer = RoFormerv2Tokenizer.from_pretrained('roformer_v2_chinese_char_base') model = RoFormerv2ForSequenceClassification.from_pretrained('roformer_v2_chinese_char_base') inputs = tokenizer("欢迎使用百度飞桨!") inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()} logits = model(**inputs)
- class RoFormerv2ForTokenClassification(config: RoFormerv2Config)[源代码]#
-
RoFormerv2 Model with a linear layer on top of the hidden-states output layer, designed for token classification tasks like NER tasks.
- 参数:
roformerv2 (
RoFormerv2Model
) -- An instance ofpaddlenlp.transformers.RoFormerv2Model
.num_labels (int, optional) -- The number of classes. Default to
2
.dropout (float, optional) -- The dropout probability for output of RoFormerv2. If None, use the same value as
hidden_dropout_prob
ofpaddlenlp.transformers.RoFormerv2Model
instance. Defaults toNone
.
- forward(input_ids, token_type_ids=None, attention_mask=None)[源代码]#
- 参数:
input_ids (Tensor) -- See
RoFormerv2Model
.token_type_ids (Tensor, optional) -- See
RoFormerv2Model
.attention_mask (Tensor, optional) -- See
RoFormerv2Model
.
- 返回:
Returns tensor
logits
, a tensor of the input token classification logits. Shape as[batch_size, sequence_length, num_labels]
and dtype asfloat32
.- 返回类型:
Tensor
示例
import paddle from paddlenlp.transformers import RoFormerv2ForTokenClassification, RoFormerv2Tokenizer tokenizer = RoFormerv2Tokenizer.from_pretrained('roformer_v2_chinese_char_base') model = RoFormerv2ForTokenClassification.from_pretrained('roformer_v2_chinese_char_base') inputs = tokenizer("欢迎使用百度飞桨!") inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()} logits = model(**inputs)
- class RoFormerv2ForQuestionAnswering(config: RoFormerv2Config)[源代码]#
-
RoFormerv2 with a linear layer on top of the hidden-states output to compute
span_start_logits
andspan_end_logits
, designed for question-answering tasks like SQuAD.- 参数:
roformerv2 (
RoFormerv2Model
) -- An instance of RoFormerv2Model.dropout (float, optional) -- The dropout probability for output of RoFormerv2. If None, use the same value as
hidden_dropout_prob
ofRoFormerv2Model
instanceroformerv2
. Defaults toNone
.
- forward(input_ids, token_type_ids=None, attention_mask=None)[源代码]#
The RoFormerv2ForQuestionAnswering forward method, overrides the __call__() special method.
- 参数:
input_ids (Tensor) -- See
RoFormerv2Model
.token_type_ids (Tensor, optional) -- See
RoFormerv2Model
.attention_mask (Tensor, optional) -- See
RoFormerv2Model
.
- 返回:
Returns tuple (
start_logits
,end_logits
).With the fields:
start_logits
(Tensor):A tensor of the input token classification logits, indicates the start position of the labelled span. Its data type should be float32 and its shape is [batch_size, sequence_length].
end_logits
(Tensor):A tensor of the input token classification logits, indicates the end position of the labelled span. Its data type should be float32 and its shape is [batch_size, sequence_length].
- 返回类型:
tuple
示例
import paddle from paddlenlp.transformers import RoFormerv2ForQuestionAnswering, RoFormerv2Tokenizer tokenizer = RoFormerv2Tokenizer.from_pretrained('roformer_v2_chinese_char_base') model = RoFormerv2ForQuestionAnswering.from_pretrained('roformer_v2_chinese_char_base') inputs = tokenizer("欢迎使用百度飞桨!") inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()} outputs = model(**inputs) start_logits = outputs[0] end_logits = outputs[1]
- class RoFormerv2ForMultipleChoice(config: RoFormerv2Config)[源代码]#
-
RoFormerv2 Model with a linear layer on top of the hidden-states output layer, designed for multiple choice tasks like RocStories/SWAG tasks.
- 参数:
roformerv2 (
RoFormerv2Model
) -- An instance of RoFormerv2Model.num_choices (int, optional) -- The number of choices. Defaults to
2
.dropout (float, optional) -- The dropout probability for output of RoFormerv2. If None, use the same value as
hidden_dropout_prob
ofRoFormerv2Model
instanceroformerv2
. Defaults to None.
- forward(input_ids, token_type_ids=None, attention_mask=None)[源代码]#
The RoFormerv2ForMultipleChoice forward method, overrides the __call__() special method.
- 参数:
input_ids (Tensor) -- See
RoFormerv2Model
and shape as [batch_size, num_choice, sequence_length].token_type_ids (Tensor, optional) -- See
RoFormerv2Model
and shape as [batch_size, num_choice, sequence_length].attention_mask (list, optional) -- See
RoFormerv2Model
and shape as [batch_size, num_choice, sequence_length].
- 返回:
Returns tensor
reshaped_logits
, a tensor of the multiple choice classification logits. Shape as[batch_size, num_choice]
and dtype asfloat32
.- 返回类型:
Tensor
示例
import paddle from paddlenlp.transformers import RoFormerv2ForMultipleChoice, RoFormerv2Tokenizer from paddlenlp.data import Pad tokenizer = RoFormerv2Tokenizer.from_pretrained('roformer_v2_chinese_char_base') model = RoFormerv2ForMultipleChoice.from_pretrained('roformer_v2_chinese_char_base', num_choices=2) 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"]) inputs = tokenizer(text, text_pair) input_ids = Pad(axis=0, pad_val=tokenizer.pad_token_id)(inputs["input_ids"]) token_type_ids = Pad(axis=0, pad_val=tokenizer.pad_token_type_id)(inputs["token_type_ids"]) reshaped_logits = model( input_ids=paddle.to_tensor(input_ids, dtype="int64"), token_type_ids=paddle.to_tensor(token_type_ids, dtype="int64"), ) print(reshaped_logits.shape) # [2, 2]