# coding:utf-8
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# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddlenlp.seq2vec.encoder import BoWEncoder, LSTMEncoder
from paddlenlp.transformers import SkepConfig, SkepModel, SkepPretrainedModel
[docs]class BoWModel(nn.Layer):
"""
This class implements the Bag of Words Classification Network model to classify texts.
At a high level, the model starts by embedding the tokens and running them through
a word embedding. Then, we encode these representations with a `BoWEncoder`.
Lastly, we take the output of the encoder to create a final representation,
which is passed through some feed-forward layers to output a logits (`output_layer`).
Args:
vocab_size(int): The vocab size that used to create the embedding.
num_class(int): The num class of the classifier.
emb_dim(int. optional): The size of the embedding, default value is 128.
padding_idx(int, optional): The padding value in the embedding, the padding_idx of embedding value will
not be updated, the default value is 0.
hidden_size(int, optional): The output size of linear that after the bow, default value is 128.
fc_hidden_size(int, optional): The output size of linear that after the first linear, default value is 96.
"""
def __init__(self, vocab_size, num_classes, emb_dim=128, padding_idx=0, hidden_size=128, fc_hidden_size=96):
super().__init__()
self.embedder = nn.Embedding(vocab_size, emb_dim, padding_idx=padding_idx)
self.bow_encoder = BoWEncoder(emb_dim)
self.fc1 = nn.Linear(self.bow_encoder.get_output_dim(), hidden_size)
self.fc2 = nn.Linear(hidden_size, fc_hidden_size)
self.output_layer = nn.Linear(fc_hidden_size, num_classes)
[docs] def forward(self, text, seq_len=None):
# Shape: (batch_size, num_tokens, embedding_dim)
embedded_text = self.embedder(text)
# Shape: (batch_size, embedding_dim)
summed = self.bow_encoder(embedded_text)
encoded_text = paddle.tanh(summed)
# Shape: (batch_size, hidden_size)
fc1_out = paddle.tanh(self.fc1(encoded_text))
# Shape: (batch_size, fc_hidden_size)
fc2_out = paddle.tanh(self.fc2(fc1_out))
# Shape: (batch_size, num_classes)
logits = self.output_layer(fc2_out)
return logits
[docs]class LSTMModel(nn.Layer):
"""
This class implements the Bag of Words Classification Network model to classify texts.
At a high level, the model starts by embedding the tokens and running them through
a word embedding. Then, we encode these representations with a `BoWEncoder`.
Lastly, we take the output of the encoder to create a final representation,
which is passed through some feed-forward layers to output a logits (`output_layer`).
Args:
vocab_size(int): The vocab size that used to create the embedding.
num_class(int): The num class of the classifier.
emb_dim(int. optional): The size of the embedding, default value is 128.
padding_idx(int, optional): The padding value in the embedding, the padding_idx of embedding value will
not be updated, the default value is 0.
lstm_hidden_size(int, optional): The output size of the lstm, default value 198.
direction(string, optional): The direction of lstm, default value is `forward`.
lstm_layers(string, optional): The num of lstm layer.
dropout(float, optional): The dropout rate of lstm.
pooling_type(float, optional): The pooling type of lstm. Default value is None,
if `pooling_type` is None, then the LSTMEncoder will return the hidden state of the last time step at last layer as a single vector.
"""
def __init__(
self,
vocab_size,
num_classes,
emb_dim=128,
padding_idx=0,
lstm_hidden_size=198,
direction="forward",
lstm_layers=1,
dropout_rate=0.0,
pooling_type=None,
fc_hidden_size=96,
):
super().__init__()
self.embedder = nn.Embedding(num_embeddings=vocab_size, embedding_dim=emb_dim, padding_idx=padding_idx)
self.lstm_encoder = LSTMEncoder(
emb_dim,
lstm_hidden_size,
num_layers=lstm_layers,
direction=direction,
dropout=dropout_rate,
pooling_type=pooling_type,
)
self.fc = nn.Linear(self.lstm_encoder.get_output_dim(), fc_hidden_size)
self.output_layer = nn.Linear(fc_hidden_size, num_classes)
[docs] def forward(self, text, seq_len):
# Shape: (batch_size, num_tokens, embedding_dim)
embedded_text = self.embedder(text)
# Shape: (batch_size, num_tokens, num_directions*lstm_hidden_size)
# num_directions = 2 if direction is 'bidirect'
# if not, num_directions = 1
text_repr = self.lstm_encoder(embedded_text, sequence_length=seq_len)
# Shape: (batch_size, fc_hidden_size)
fc_out = paddle.tanh(self.fc(text_repr))
# Shape: (batch_size, num_classes)
logits = self.output_layer(fc_out)
probs = F.softmax(logits, axis=1)
idx = paddle.argmax(probs, axis=1).numpy()
return idx, probs
[docs]class SkepSequenceModel(SkepPretrainedModel):
def __init__(self, config: SkepConfig):
super(SkepSequenceModel, self).__init__(config)
self.skep = SkepModel(config)
self.num_labels = config.num_labels
self.dropout = nn.Dropout(
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.classifier = nn.Linear(config.hidden_size, self.num_labels)
[docs] def forward(self, input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None):
outputs = self.skep(
input_ids, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
probs = F.softmax(logits, axis=1)
idx = paddle.argmax(probs, axis=1)
return idx, probs
