modeling#
- class GAUAlphaModel(config: GAUAlphaConfig)[源代码]#
-
The bare GAUAlpha 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.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
inGAUAlphaModel
. Also is the vocab size of token embedding matrix. Defines the number of different tokens that can be represented by theinputs_ids
passed when callingGAUAlphaModel
.hidden_size (int, optional) -- Dimensionality of the, encoder layers and pooler layer. Defaults to
768
.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
.num_hidden_layers (int, optional) -- Number of hidden layers in the gau_alpha encoder. Defaults to
12
.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
.attention_key_size (int, optional) -- The dimensionality of the key used in the gau layer. Defaults to
128
.norm_eps (float, optional) -- The epsilon value used in the normalization layer. Defaults to
1e-12
.pad_token_id (int, optional) -- The index of padding token in the token vocabulary. Defaults to
0
.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 gau in all encoder layers to drop some attention target. Defaults to
0.1
.hidden_act (str, optional) -- The activation function used in gau layer. Defaults to
swish
.use_bias (bool, optional) -- Whether or not use bias. Defaults to
False
.normalization (str, optional) -- The normalization method used in gau layer. Defaults to
softmax_plus
.attention_scale (bool, optional) -- Whether or not to scale the attention scores. Defaults to
True
.
- set_input_embeddings(value)[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)[源代码]#
The GAUAlphaModel 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].attention_mask (Tensor, optional) -- Mask used in gau 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. It is a tensor with shape broadcasted to[batch_size, sequence_length, sequence_length]
. Defaults toNone
, which means nothing needed to be prevented attention to.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.
- 返回:
- Returns
last_hidden_state
(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].
- Returns
- 返回类型:
tuple
示例
import paddle from paddlenlp.transformers import GAUAlphaModel, GAUAlphaTokenizer tokenizer = GAUAlphaTokenizer.from_pretrained('chinese_GAU-alpha-char_L-24_H-768') model = GAUAlphaModel.from_pretrained('chinese_GAU-alpha-char_L-24_H-768') inputs = tokenizer("欢迎使用百度飞桨!") inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()} last_hidden_state = model(**inputs)
- class GAUAlphaForMaskedLM(config: GAUAlphaConfig)[源代码]#
-
GAUAlpha Model with a
masked language modeling
head on top.- 参数:
gau_alpha (
GAUAlphaModel
) -- An instance ofGAUAlphaModel
.
- forward(input_ids, token_type_ids=None, attention_mask=None)[源代码]#
- 参数:
input_ids (Tensor) -- See
GAUAlphaModel
.token_type_ids (Tensor, optional) -- See
GAUAlphaModel
.attention_mask (Tensor, optional) -- See
GAUAlphaModel
.
- 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 GAUAlphaForMaskedLM, GAUAlphaTokenizer tokenizer = GAUAlphaTokenizer.from_pretrained('chinese_GAU-alpha-char_L-24_H-768') model = GAUAlphaForMaskedLM.from_pretrained('chinese_GAU-alpha-char_L-24_H-768') 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 GAUAlphaPretrainedModel(*args, **kwargs)[源代码]#
-
An abstract class for pretrained GAU-alpha models. It provides GAU-alpha 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#
GAUAlphaConfig
的别名
- base_model_class#
GAUAlphaModel
的别名
- class GAUAlphaForSequenceClassification(config: GAUAlphaConfig)[源代码]#
-
GAUAlpha Model with a linear layer on top of the output layer, designed for sequence classification/regression tasks like GLUE tasks.
- 参数:
gau_alpha (
GAUAlphaModel
) -- An instance ofpaddlenlp.transformers.GAUAlphaModel
.num_labels (int, optional) -- The number of classes. Default to
2
.dropout (float, optional) -- The dropout probability for output of GAUAlpha. If None, use the same value as
hidden_dropout_prob
ofpaddlenlp.transformers.GAUAlphaModel
instance. Defaults toNone
.
- forward(input_ids, token_type_ids=None, attention_mask=None)[源代码]#
- 参数:
input_ids (Tensor) -- See
GAUAlphaModel
.token_type_ids (Tensor, optional) -- See
GAUAlphaModel
.attention_mask (Tensor, optional) -- See
GAUAlphaModel
.
- 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 GAUAlphaForSequenceClassification, GAUAlphaTokenizer tokenizer = GAUAlphaTokenizer.from_pretrained('chinese_GAU-alpha-char_L-24_H-768') model = GAUAlphaForSequenceClassification.from_pretrained('chinese_GAU-alpha-char_L-24_H-768') 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 GAUAlphaForTokenClassification(config: GAUAlphaConfig)[源代码]#
-
GAUAlpha Model with a linear layer on top of the hidden-states output layer, designed for token classification tasks like NER tasks.
- 参数:
gau_alpha (
GAUAlphaModel
) -- An instance ofpaddlenlp.transformers.GAUAlphaModel
.num_labels (int, optional) -- The number of classes. Default to
2
.dropout (float, optional) -- The dropout probability for output of GAUAlpha. If None, use the same value as
hidden_dropout_prob
ofpaddlenlp.transformers.GAUAlphaModel
instance. Defaults toNone
.
- forward(input_ids, token_type_ids=None, attention_mask=None)[源代码]#
- 参数:
input_ids (Tensor) -- See
GAUAlphaModel
.token_type_ids (Tensor, optional) -- See
GAUAlphaModel
.attention_mask (Tensor, optional) -- See
GAUAlphaModel
.
- 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 GAUAlphaForTokenClassification, GAUAlphaTokenizer tokenizer = GAUAlphaTokenizer.from_pretrained('chinese_GAU-alpha-char_L-24_H-768') model = GAUAlphaForTokenClassification.from_pretrained('chinese_GAU-alpha-char_L-24_H-768') 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 GAUAlphaForQuestionAnswering(config: GAUAlphaConfig)[源代码]#
-
GAUAlpha 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:
gau_alpha (
GAUAlphaModel
) -- An instance of GAUAlphaModel.dropout (float, optional) -- The dropout probability for output of GAUAlpha. If None, use the same value as
hidden_dropout_prob
ofGAUAlphaModel
instancegau_alpha
. Defaults toNone
.
- forward(input_ids, token_type_ids=None, attention_mask=None)[源代码]#
The GAUAlphaForQuestionAnswering forward method, overrides the __call__() special method.
- 参数:
input_ids (Tensor) -- See
GAUAlphaModel
.token_type_ids (Tensor, optional) -- See
GAUAlphaModel
.attention_mask (Tensor, optional) -- See
GAUAlphaModel
.
- 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 GAUAlphaForQuestionAnswering, GAUAlphaTokenizer tokenizer = GAUAlphaTokenizer.from_pretrained('chinese_GAU-alpha-char_L-24_H-768') model = GAUAlphaForQuestionAnswering.from_pretrained('chinese_GAU-alpha-char_L-24_H-768') 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 GAUAlphaForMultipleChoice(config: GAUAlphaConfig)[源代码]#
-
GAUAlpha Model with a linear layer on top of the hidden-states output layer, designed for multiple choice tasks like RocStories/SWAG tasks.
- 参数:
gau_alpha (
GAUAlphaModel
) -- An instance of GAUAlphaModel.num_choices (int, optional) -- The number of choices. Defaults to
2
.dropout (float, optional) -- The dropout probability for output of GAUAlpha. If None, use the same value as
hidden_dropout_prob
ofGAUAlphaModel
instancegau_alpha
. Defaults to None.
- forward(input_ids, token_type_ids=None, attention_mask=None)[源代码]#
The GAUAlphaForMultipleChoice forward method, overrides the __call__() special method.
- 参数:
input_ids (Tensor) -- See
GAUAlphaModel
and shape as [batch_size, num_choice, sequence_length].token_type_ids (Tensor, optional) -- See
GAUAlphaModel
and shape as [batch_size, num_choice, sequence_length].attention_mask (list, optional) -- See
GAUAlphaModel
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 GAUAlphaForMultipleChoice, GAUAlphaTokenizer from paddlenlp.data import Pad tokenizer = GAUAlphaTokenizer.from_pretrained('chinese_GAU-alpha-char_L-24_H-768') model = GAUAlphaForMultipleChoice.from_pretrained('chinese_GAU-alpha-char_L-24_H-768', 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]