modeling
- class PPMiniLMModel(config: PPMiniLMConfig)[源代码]
-
The bare PPMiniLM 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:
config (
PPMiniLMConfig
) -- An instance of PPMiniLMConfig used to construct PPMiniLMModel.
- 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, token_type_ids=None, position_ids=None, attention_mask=None)[源代码]
- 参数:
input_ids (Tensor) -- If
input_ids
is a Tensor object, it is an indices of input sequence tokens in the vocabulary. They are numerical representations of tokens that build the input sequence. It's data type should beint64
and has a shape of [batch_size, sequence_length].token_type_ids (Tensor, string, optional) --
If
token_type_ids
is a Tensor object: 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.position_ids (Tensor, optional) -- Indices of positions of each input sequence tokens in the position embeddings. Selected in the range
[0, max_position_embeddings - 1]
. Shape as[batch_size, num_tokens]
and dtype as int64. Defaults toNone
.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 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]. We use whole-word-mask in PPMiniLM, so the whole word will have the same value. For example, "使用" as a word, "使" and "用" will have the same value. Defaults toNone
, which means nothing needed to be prevented attention to.
- 返回:
Returns tuple (
sequence_output
,pooled_output
).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].
pooled_output
(Tensor):The output of first token (
[CLS]
) in sequence. We "pool" the model by simply taking the hidden state corresponding to the first token. Its data type should be float32 and its shape is [batch_size, hidden_size].
- 返回类型:
tuple
示例
import paddle from paddlenlp.transformers import PPMiniLMModel, PPMiniLMTokenizer tokenizer = PPMiniLMTokenizer.from_pretrained('ppminilm-6l-768h') model = PPMiniLMModel.from_pretrained('ppminilm-6l-768h') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} sequence_output, pooled_output = model(**inputs)
- class PPMiniLMPretrainedModel(*args, **kwargs)[源代码]
-
An abstract class for pretrained PPMiniLM models. It provides PPMiniLM related
model_config_file
,pretrained_init_configuration
,resource_files_names
,pretrained_resource_files_map
,base_model_prefix
for downloading and loading pretrained models. Refer toPretrainedModel
for more details.- config_class
PPMiniLMConfig
的别名
- base_model_class
PPMiniLMModel
的别名
- class PPMiniLMForSequenceClassification(config: PPMiniLMConfig)[源代码]
-
PPMiniLM Model with a linear layer on top of the output layer, designed for sequence classification/regression tasks like GLUE tasks.
- 参数:
ppminilm (PPMiniLMModel) -- An instance of
paddlenlp.transformers.PPMiniLMModel
.num_classes (int, optional) -- The number of classes. Default to
2
.dropout (float, optional) -- The dropout probability for output of PPMiniLM. If None, use the same value as
hidden_dropout_prob
ofpaddlenlp.transformers.PPMiniLMModel
instance. Defaults toNone
.
- forward(input_ids, token_type_ids=None, position_ids=None, attention_mask=None)[源代码]
- 参数:
input_ids (Tensor) -- See
PPMiniLMModel
.token_type_ids (Tensor, optional) -- See
PPMiniLMModel
.position_ids (Tensor, optional) -- See
PPMiniLMModel
.attention_mask (Tensor, optional) -- See
MiniLMModel
.
- 返回:
Returns tensor
logits
, a tensor of the input text classification logits. Shape as[batch_size, num_classes]
and dtype as float32.- 返回类型:
Tensor
示例
import paddle from paddlenlp.transformers import PPMiniLMForSequenceClassification, PPMiniLMTokenizer tokenizer = PPMiniLMTokenizer.from_pretrained('ppminilm-6l-768h') model = PPMiniLMForSequenceClassification.from_pretrained('ppminilm-6l-768h0') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs)
- class PPMiniLMForQuestionAnswering(config: PPMiniLMConfig)[源代码]
-
PPMiniLM Model 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:
ppminilm (
PPMiniLMModel
) -- An instance ofPPMiniLMModel
.
- forward(input_ids, token_type_ids=None, position_ids=None, attention_mask=None)[源代码]
- 参数:
input_ids (Tensor) -- See
PPMiniLMModel
.token_type_ids (Tensor, optional) -- See
PPMiniLMModel
.position_ids (Tensor, optional) -- See
PPMiniLMModel
.attention_mask (Tensor, optional) -- See
PPMiniLMModel
.
- 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 PPMiniLMForQuestionAnswering, PPMiniLMTokenizer tokenizer = PPMiniLMTokenizer.from_pretrained('ppminilm-6l-768h') model = PPMiniLMForQuestionAnswering.from_pretrained('ppminilm-6l-768h') inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!") inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()} logits = model(**inputs)
- class PPMiniLMForMultipleChoice(config: PPMiniLMConfig)[源代码]
-
PPMiniLM Model with a linear layer on top of the hidden-states output layer, designed for multiple choice tasks like RocStories/SWAG tasks.
- 参数:
ppminilm (
PPMiniLMModel
) -- An instance of PPMiniLMModel.num_choices (int, optional) -- The number of choices. Defaults to
2
.dropout (float, optional) -- The dropout probability for output of PPMiniLM. If None, use the same value as
hidden_dropout_prob
ofPPMiniLMModel
instanceppminilm
. Defaults to None.
- forward(input_ids, token_type_ids=None, position_ids=None, attention_mask=None)[源代码]
The PPMiniLMForMultipleChoice forward method, overrides the __call__() special method.
- 参数:
input_ids (Tensor) -- See
PPMiniLMModel
and shape as [batch_size, num_choice, sequence_length].token_type_ids (Tensor, optional) -- See
PPMiniLMModel
and shape as [batch_size, num_choice, sequence_length].position_ids (Tensor, optional) -- See
PPMiniLMModel
and shape as [batch_size, num_choice, sequence_length].attention_mask (list, optional) -- See
PPMiniLMModel
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