modeling#

class PPMiniLMModel(config: PPMiniLMConfig)[source]#

Bases: PPMiniLMPretrainedModel

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.

Parameters:

config (PPMiniLMConfig) – An instance of PPMiniLMConfig used to construct PPMiniLMModel.

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, token_type_ids=None, position_ids=None, attention_mask=None)[source]#
Parameters:

  • 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 be int64 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]. If type_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 to None, 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 to None.

  • 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 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]. 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 to None, which means nothing needed to be prevented attention to.

Returns:

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].

Return type:

tuple

Example

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)[source]#

Bases: PretrainedModel

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 to PretrainedModel for more details.

config_class#

alias of PPMiniLMConfig

base_model_class#

alias of PPMiniLMModel

class PPMiniLMForSequenceClassification(config: PPMiniLMConfig)[source]#

Bases: PPMiniLMPretrainedModel

PPMiniLM Model with a linear layer on top of the output layer, designed for sequence classification/regression tasks like GLUE tasks.

Parameters:

  • 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 of paddlenlp.transformers.PPMiniLMModel instance. Defaults to None.

forward(input_ids, token_type_ids=None, position_ids=None, attention_mask=None)[source]#
Parameters:

  • input_ids (Tensor) – See PPMiniLMModel.

  • token_type_ids (Tensor, optional) – See PPMiniLMModel.

  • position_ids (Tensor, optional) – See PPMiniLMModel.

  • attention_mask (Tensor, optional) – See MiniLMModel.

Returns:

Returns tensor logits, a tensor of the input text classification logits. Shape as [batch_size, num_classes] and dtype as float32.

Return type:

Tensor

Example

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)[source]#

Bases: PPMiniLMPretrainedModel

PPMiniLM 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:

ppminilm (PPMiniLMModel) – An instance of PPMiniLMModel.

forward(input_ids, token_type_ids=None, position_ids=None, attention_mask=None)[source]#
Parameters:
Returns:

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].

Return type:

tuple

Example

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)[source]#

Bases: PPMiniLMPretrainedModel

PPMiniLM Model with a linear layer on top of the hidden-states output layer, designed for multiple choice tasks like RocStories/SWAG tasks.

Parameters:

  • 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 of PPMiniLMModel instance ppminilm. Defaults to None.

forward(input_ids, token_type_ids=None, position_ids=None, attention_mask=None)[source]#

The PPMiniLMForMultipleChoice forward method, overrides the __call__() special method.

Parameters:

  • 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].

Returns:

Returns tensor reshaped_logits, a tensor of the multiple choice classification logits. Shape as [batch_size, num_choice] and dtype as float32.

Return type:

Tensor