modeling

class ErnieDocModel(num_hidden_layers, num_attention_heads, hidden_size, hidden_dropout_prob, attention_dropout_prob, relu_dropout, hidden_act, memory_len, vocab_size, max_position_embeddings, task_type_vocab_size=3, normalize_before=False, epsilon=1e-05, rel_pos_params_sharing=False, initializer_range=0.02, pad_token_id=0, cls_token_idx=- 1)[source]

Bases: paddlenlp.transformers.ernie_doc.modeling.ErnieDocPretrainedModel

The bare ERNIE-Doc Model outputting raw hidden-states.

This model inherits from PretrainedModel. Refer to the superclass documentation for the generic methods.

This model is also a 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

  • num_hidden_layers (int) – The number of hidden layers in the Transformer encoder.

  • num_attention_heads (int) – Number of attention heads for each attention layer in the Transformer encoder.

  • hidden_size (int) – Dimensionality of the embedding layers, encoder layers and pooler layer.

  • hidden_dropout_prob (int) – The dropout probability for all fully connected layers in the embeddings and encoder.

  • attention_dropout_prob (int) – The dropout probability used in MultiHeadAttention in all encoder layers to drop some attention target.

  • relu_dropout (int) – The dropout probability of FFN.

  • hidden_act (str) – The non-linear activation function of FFN.

  • memory_len (int) – The number of tokens to cache. If not 0, the last memory_len hidden states in each layer will be cached into memory.

  • vocab_size (int) – Vocabulary size of inputs_ids in ErnieDocModel. Also is the vocab size of token embedding matrix. Defines the number of different tokens that can be represented by the inputs_ids passed when calling ErnieDocModel.

  • max_position_embeddings (int) – The maximum value of the dimensionality of position encoding, which dictates the maximum supported length of an input sequence. Defaults to 512.

  • task_type_vocab_size (int, optional) – The vocabulary size of the token_type_ids. Defaults to 3.

  • normalize_before (bool, optional) – Indicate whether to put layer normalization into preprocessing of MHA and FFN sub-layers. If True, pre-process is layer normalization and post-precess includes dropout, residual connection. Otherwise, no pre-process and post-precess includes dropout, residual connection, layer normalization. Defaults to False.

  • epsilon (float, optional) – The epsilon parameter used in paddle.nn.LayerNorm for initializing layer normalization layers. Defaults to 1e-5.

  • rel_pos_params_sharing (bool, optional) – Whether to share the relative position parameters. Defaults to False.

  • initializer_range (float, optional) – The standard deviation of the normal initializer for initializing all weight matrices. Defaults to 0.02.

  • pad_token_id (int, optional) – The token id of [PAD] token whose parameters won’t be updated when training. Defaults to 0.

  • cls_token_idx (int, optional) – The token id of [CLS] token. Defaults to -1.

forward(input_ids, memories, token_type_ids, position_ids, attn_mask)[source]

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

Parameters

  • input_ids (Tensor) – 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, 1].

  • memories (List[Tensor]) – A list of length n_layers with each Tensor being a pre-computed hidden-state for each layer. Each Tensor has a dtype float32 and a shape of [batch_size, sequence_length, hidden_size].

  • token_type_ids (Tensor) –

    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, 1]. Defaults to None, which means no segment embeddings is added to token embeddings.

  • position_ids (Tensor) – Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.max_position_embeddings - 1]. Shape as (batch_sie, num_tokens) and dtype as int32 or int64.

  • attn_mask (Tensor) – 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 ERNIE, 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 (encoder_output, pooled_output, new_mem).

With the fields:

  • encoder_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].

  • new_mem (List[Tensor]):

    A list of pre-computed hidden-states. The length of the list is n_layers. Each element in the list is a Tensor with dtype float32 and shape as [batch_size, memory_length, hidden_size].

Return type

tuple

Example

import numpy as np
import paddle
from paddlenlp.transformers import ErnieDocModel
from paddlenlp.transformers import ErnieDocTokenizer

def get_related_pos(insts, seq_len, memory_len=128):
    beg = seq_len + seq_len + memory_len
    r_position = [list(range(beg - 1, seq_len - 1, -1)) + \
                list(range(0, seq_len)) for i in range(len(insts))]
    return np.array(r_position).astype('int64').reshape([len(insts), beg, 1])

tokenizer = ErnieDocTokenizer.from_pretrained('ernie-doc-base-zh')
model = ErnieDocModel.from_pretrained('ernie-doc-base-zh')

inputs = tokenizer("欢迎使用百度飞桨！")
inputs = {k:paddle.to_tensor([v + [0] * (128-len(v))]).unsqueeze(-1) for (k, v) in inputs.items()}

memories = [paddle.zeros([1, 128, 768], dtype="float32") for _ in range(12)]
position_ids = paddle.to_tensor(get_related_pos(inputs['input_ids'], 128, 128))
attn_mask = paddle.ones([1, 128, 1])

inputs['memories'] = memories
inputs['position_ids'] = position_ids
inputs['attn_mask'] = attn_mask

outputs = model(**inputs)

encoder_output = outputs[0]
pooled_output = outputs[1]
new_mem = outputs[2]
class ErnieDocPretrainedModel(*args, **kwargs)[source]

Bases: paddlenlp.transformers.model_utils.PretrainedModel

An abstract class for pretrained ErnieDoc models. It provides ErnieDoc 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.

base_model_class

alias of paddlenlp.transformers.ernie_doc.modeling.ErnieDocModel

class ErnieDocForSequenceClassification(ernie_doc, num_classes=2, dropout=0.1)[source]

Bases: paddlenlp.transformers.ernie_doc.modeling.ErnieDocPretrainedModel

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

Parameters

  • ernie_doc (ErnieDocModel) – An instance of ErnieDocModel.

  • num_classes (int) – The number of classes.

  • dropout (float, optional) – The dropout ratio of last output. Default to 0.1.

forward(input_ids, memories, token_type_ids, position_ids, attn_mask)[source]

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

Parameters
Returns

Returns tuple (logits, mem).

With the fields:

  • logits (Tensor):

    A tensor containing the [CLS] of hidden-states of the model at the output of last layer. Each Tensor has a data type of float32 and has a shape of [batch_size, num_classes].

  • mem (List[Tensor]):

    A list of pre-computed hidden-states. The length of the list is n_layers. Each element in the list is a Tensor with dtype float32 and has a shape of [batch_size, memory_length, hidden_size].

Return type

tuple

Example

import numpy as np
import paddle
from paddlenlp.transformers import ErnieDocForSequenceClassification
from paddlenlp.transformers import ErnieDocTokenizer

def get_related_pos(insts, seq_len, memory_len=128):
    beg = seq_len + seq_len + memory_len
    r_position = [list(range(beg - 1, seq_len - 1, -1)) + \
                list(range(0, seq_len)) for i in range(len(insts))]
    return np.array(r_position).astype('int64').reshape([len(insts), beg, 1])

tokenizer = ErnieDocTokenizer.from_pretrained('ernie-doc-base-zh')
model = ErnieDocForSequenceClassification.from_pretrained('ernie-doc-base-zh', num_classes=2)

inputs = tokenizer("欢迎使用百度飞桨！")
inputs = {k:paddle.to_tensor([v + [0] * (128-len(v))]).unsqueeze(-1) for (k, v) in inputs.items()}

memories = [paddle.zeros([1, 128, 768], dtype="float32") for _ in range(12)]
position_ids = paddle.to_tensor(get_related_pos(inputs['input_ids'], 128, 128))
attn_mask = paddle.ones([1, 128, 1])

inputs['memories'] = memories
inputs['position_ids'] = position_ids
inputs['attn_mask'] = attn_mask

outputs = model(**inputs)

logits = outputs[0]
mem = outputs[1]
class ErnieDocForTokenClassification(ernie_doc, num_classes=2, dropout=0.1)[source]

Bases: paddlenlp.transformers.ernie_doc.modeling.ErnieDocPretrainedModel

ErnieDoc Model with a linear layer on top of the hidden-states output layer, designed for token classification tasks like NER tasks.

Parameters

  • ernie_doc (ErnieDocModel) – An instance of ErnieDocModel.

  • num_classes (int) – The number of classes.

  • dropout (float, optional) – The dropout ratio of last output. Default to 0.1.

forward(input_ids, memories, token_type_ids, position_ids, attn_mask)[source]

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

Parameters
Returns

Returns tuple (logits, mem).

With the fields:

  • logits (Tensor):

    A tensor containing the hidden-states of the model at the output of last layer. Each Tensor has a data type of float32 and has a shape of [batch_size, sequence_length, num_classes].

  • mem (List[Tensor]):

    A list of pre-computed hidden-states. The length of the list is n_layers. Each element in the list is a Tensor with dtype float32 and has a shape of [batch_size, memory_length, hidden_size].

Return type

tuple

Example

import numpy as np
import paddle
from paddlenlp.transformers import ErnieDocForTokenClassification
from paddlenlp.transformers import ErnieDocTokenizer

def get_related_pos(insts, seq_len, memory_len=128):
    beg = seq_len + seq_len + memory_len
    r_position = [list(range(beg - 1, seq_len - 1, -1)) + \
                list(range(0, seq_len)) for i in range(len(insts))]
    return np.array(r_position).astype('int64').reshape([len(insts), beg, 1])

tokenizer = ErnieDocTokenizer.from_pretrained('ernie-doc-base-zh')
model = ErnieDocForTokenClassification.from_pretrained('ernie-doc-base-zh', num_classes=2)

inputs = tokenizer("欢迎使用百度飞桨！")
inputs = {k:paddle.to_tensor([v + [0] * (128-len(v))]).unsqueeze(-1) for (k, v) in inputs.items()}

memories = [paddle.zeros([1, 128, 768], dtype="float32") for _ in range(12)]
position_ids = paddle.to_tensor(get_related_pos(inputs['input_ids'], 128, 128))
attn_mask = paddle.ones([1, 128, 1])

inputs['memories'] = memories
inputs['position_ids'] = position_ids
inputs['attn_mask'] = attn_mask

outputs = model(**inputs)

logits = outputs[0]
mem = outputs[1]
class ErnieDocForQuestionAnswering(ernie_doc, dropout=0.1)[source]

Bases: paddlenlp.transformers.ernie_doc.modeling.ErnieDocPretrainedModel

ErnieDoc 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

  • ernie_doc (ErnieDocModel) – An instance of ErnieDocModel.

  • dropout (float, optional) – The dropout ratio of last output. Default to 0.1.

forward(input_ids, memories, token_type_ids, position_ids, attn_mask)[source]

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

Parameters
Returns

Returns tuple (start_logits, end_logits, mem).

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].

  • mem (List[Tensor]):

    A list of pre-computed hidden-states. The length of the list is n_layers. Each element in the list is a Tensor with dtype float32 and has a shape of [batch_size, memory_length, hidden_size].

Return type

tuple

Example

import numpy as np
import paddle
from paddlenlp.transformers import ErnieDocForQuestionAnswering
from paddlenlp.transformers import ErnieDocTokenizer

def get_related_pos(insts, seq_len, memory_len=128):
    beg = seq_len + seq_len + memory_len
    r_position = [list(range(beg - 1, seq_len - 1, -1)) + \
                list(range(0, seq_len)) for i in range(len(insts))]
    return np.array(r_position).astype('int64').reshape([len(insts), beg, 1])

tokenizer = ErnieDocTokenizer.from_pretrained('ernie-doc-base-zh')
model = ErnieDocForQuestionAnswering.from_pretrained('ernie-doc-base-zh')

inputs = tokenizer("欢迎使用百度飞桨！")
inputs = {k:paddle.to_tensor([v + [0] * (128-len(v))]).unsqueeze(-1) for (k, v) in inputs.items()}

memories = [paddle.zeros([1, 128, 768], dtype="float32") for _ in range(12)]
position_ids = paddle.to_tensor(get_related_pos(inputs['input_ids'], 128, 128))
attn_mask = paddle.ones([1, 128, 1])

inputs['memories'] = memories
inputs['position_ids'] = position_ids
inputs['attn_mask'] = attn_mask

outputs = model(**inputs)

start_logits = outputs[0]
end_logits = outputs[1]
mem = outputs[2]