wehub-resource-sync
152 lines
6.7 KiB
Python
152 lines
6.7 KiB
Python
# coding:utf-8
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddlenlp.seq2vec.encoder import BoWEncoder, LSTMEncoder
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from paddlenlp.transformers import SkepConfig, SkepModel, SkepPretrainedModel
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class BoWModel(nn.Layer):
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"""
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This class implements the Bag of Words Classification Network model to classify texts.
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At a high level, the model starts by embedding the tokens and running them through
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a word embedding. Then, we encode these representations with a `BoWEncoder`.
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Lastly, we take the output of the encoder to create a final representation,
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which is passed through some feed-forward layers to output a logits (`output_layer`).
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Args:
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vocab_size(int): The vocab size that used to create the embedding.
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num_class(int): The num class of the classifier.
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emb_dim(int. optional): The size of the embedding, default value is 128.
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padding_idx(int, optional): The padding value in the embedding, the padding_idx of embedding value will
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not be updated, the default value is 0.
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hidden_size(int, optional): The output size of linear that after the bow, default value is 128.
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fc_hidden_size(int, optional): The output size of linear that after the first linear, default value is 96.
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"""
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def __init__(self, vocab_size, num_classes, emb_dim=128, padding_idx=0, hidden_size=128, fc_hidden_size=96):
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super().__init__()
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self.embedder = nn.Embedding(vocab_size, emb_dim, padding_idx=padding_idx)
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self.bow_encoder = BoWEncoder(emb_dim)
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self.fc1 = nn.Linear(self.bow_encoder.get_output_dim(), hidden_size)
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self.fc2 = nn.Linear(hidden_size, fc_hidden_size)
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self.output_layer = nn.Linear(fc_hidden_size, num_classes)
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def forward(self, text, seq_len=None):
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# Shape: (batch_size, num_tokens, embedding_dim)
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embedded_text = self.embedder(text)
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# Shape: (batch_size, embedding_dim)
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summed = self.bow_encoder(embedded_text)
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encoded_text = paddle.tanh(summed)
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# Shape: (batch_size, hidden_size)
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fc1_out = paddle.tanh(self.fc1(encoded_text))
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# Shape: (batch_size, fc_hidden_size)
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fc2_out = paddle.tanh(self.fc2(fc1_out))
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# Shape: (batch_size, num_classes)
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logits = self.output_layer(fc2_out)
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return logits
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class LSTMModel(nn.Layer):
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"""
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This class implements the Bag of Words Classification Network model to classify texts.
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At a high level, the model starts by embedding the tokens and running them through
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a word embedding. Then, we encode these representations with a `BoWEncoder`.
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Lastly, we take the output of the encoder to create a final representation,
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which is passed through some feed-forward layers to output a logits (`output_layer`).
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Args:
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vocab_size(int): The vocab size that used to create the embedding.
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num_class(int): The num class of the classifier.
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emb_dim(int. optional): The size of the embedding, default value is 128.
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padding_idx(int, optional): The padding value in the embedding, the padding_idx of embedding value will
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not be updated, the default value is 0.
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lstm_hidden_size(int, optional): The output size of the lstm, default value 198.
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direction(string, optional): The direction of lstm, default value is `forward`.
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lstm_layers(string, optional): The num of lstm layer.
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dropout(float, optional): The dropout rate of lstm.
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pooling_type(float, optional): The pooling type of lstm. Default value is None,
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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.
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"""
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def __init__(
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self,
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vocab_size,
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num_classes,
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emb_dim=128,
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padding_idx=0,
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lstm_hidden_size=198,
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direction="forward",
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lstm_layers=1,
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dropout_rate=0.0,
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pooling_type=None,
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fc_hidden_size=96,
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):
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super().__init__()
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self.embedder = nn.Embedding(num_embeddings=vocab_size, embedding_dim=emb_dim, padding_idx=padding_idx)
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self.lstm_encoder = LSTMEncoder(
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emb_dim,
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lstm_hidden_size,
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num_layers=lstm_layers,
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direction=direction,
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dropout=dropout_rate,
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pooling_type=pooling_type,
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)
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self.fc = nn.Linear(self.lstm_encoder.get_output_dim(), fc_hidden_size)
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self.output_layer = nn.Linear(fc_hidden_size, num_classes)
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def forward(self, text, seq_len):
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# Shape: (batch_size, num_tokens, embedding_dim)
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embedded_text = self.embedder(text)
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# Shape: (batch_size, num_tokens, num_directions*lstm_hidden_size)
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# num_directions = 2 if direction is 'bidirect'
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# if not, num_directions = 1
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text_repr = self.lstm_encoder(embedded_text, sequence_length=seq_len)
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# Shape: (batch_size, fc_hidden_size)
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fc_out = paddle.tanh(self.fc(text_repr))
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# Shape: (batch_size, num_classes)
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logits = self.output_layer(fc_out)
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probs = F.softmax(logits, axis=1)
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idx = paddle.argmax(probs, axis=1).numpy()
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return idx, probs
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class SkepSequenceModel(SkepPretrainedModel):
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def __init__(self, config: SkepConfig):
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super(SkepSequenceModel, self).__init__(config)
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self.skep = SkepModel(config)
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self.num_labels = config.num_labels
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self.dropout = nn.Dropout(
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config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
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)
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self.classifier = nn.Linear(config.hidden_size, self.num_labels)
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def forward(self, input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None):
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outputs = self.skep(
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input_ids, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask
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)
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pooled_output = outputs[1]
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pooled_output = self.dropout(pooled_output)
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logits = self.classifier(pooled_output)
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probs = F.softmax(logits, axis=1)
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idx = paddle.argmax(probs, axis=1)
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return idx, probs
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