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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
-
- '''
- Bert finetune and evaluation model script.
- '''
-
- import mindspore.nn as nn
- from mindspore.common.initializer import TruncatedNormal
- from mindspore.ops import operations as P
- from .bert_model import BertModel
-
- class BertCLSModel(nn.Cell):
- """
- This class is responsible for classification task evaluation, i.e. XNLI(num_labels=3),
- LCQMC(num_labels=2), Chnsenti(num_labels=2). The returned output represents the final
- logits as the results of log_softmax is proportional to that of softmax.
- """
- def __init__(self, config, is_training, num_labels=2, dropout_prob=0.0, use_one_hot_embeddings=False,
- assessment_method=""):
- super(BertCLSModel, self).__init__()
- if not is_training:
- config.hidden_dropout_prob = 0.0
- config.hidden_probs_dropout_prob = 0.0
- self.bert = BertModel(config, is_training, use_one_hot_embeddings)
- self.cast = P.Cast()
- self.weight_init = TruncatedNormal(config.initializer_range)
- self.log_softmax = P.LogSoftmax(axis=-1)
- self.dtype = config.dtype
- self.num_labels = num_labels
- self.dense_1 = nn.Dense(config.hidden_size, self.num_labels, weight_init=self.weight_init,
- has_bias=True).to_float(config.compute_type)
- self.dropout = nn.Dropout(1 - dropout_prob)
- self.assessment_method = assessment_method
-
- def construct(self, input_ids, input_mask, token_type_id):
- _, pooled_output, _ = \
- self.bert(input_ids, token_type_id, input_mask)
- cls = self.cast(pooled_output, self.dtype)
- cls = self.dropout(cls)
- logits = self.dense_1(cls)
- logits = self.cast(logits, self.dtype)
- if self.assessment_method != "spearman_correlation":
- logits = self.log_softmax(logits)
- return logits
-
- class BertSquadModel(nn.Cell):
- '''
- This class is responsible for SQuAD
- '''
- def __init__(self, config, is_training, num_labels=2, dropout_prob=0.0, use_one_hot_embeddings=False):
- super(BertSquadModel, self).__init__()
- if not is_training:
- config.hidden_dropout_prob = 0.0
- config.hidden_probs_dropout_prob = 0.0
- self.bert = BertModel(config, is_training, use_one_hot_embeddings)
- self.weight_init = TruncatedNormal(config.initializer_range)
- self.dense1 = nn.Dense(config.hidden_size, num_labels, weight_init=self.weight_init,
- has_bias=True).to_float(config.compute_type)
- self.num_labels = num_labels
- self.dtype = config.dtype
- self.log_softmax = P.LogSoftmax(axis=1)
- self.is_training = is_training
-
- def construct(self, input_ids, input_mask, token_type_id):
- sequence_output, _, _ = self.bert(input_ids, token_type_id, input_mask)
- batch_size, seq_length, hidden_size = P.Shape()(sequence_output)
- sequence = P.Reshape()(sequence_output, (-1, hidden_size))
- logits = self.dense1(sequence)
- logits = P.Cast()(logits, self.dtype)
- logits = P.Reshape()(logits, (batch_size, seq_length, self.num_labels))
- logits = self.log_softmax(logits)
- return logits
-
- class BertNERModel(nn.Cell):
- """
- This class is responsible for sequence labeling task evaluation, i.e. NER(num_labels=11).
- The returned output represents the final logits as the results of log_softmax is proportional to that of softmax.
- """
- def __init__(self, config, is_training, num_labels=11, use_crf=False, dropout_prob=0.0,
- use_one_hot_embeddings=False):
- super(BertNERModel, self).__init__()
- if not is_training:
- config.hidden_dropout_prob = 0.0
- config.hidden_probs_dropout_prob = 0.0
- self.bert = BertModel(config, is_training, use_one_hot_embeddings)
- self.cast = P.Cast()
- self.weight_init = TruncatedNormal(config.initializer_range)
- self.log_softmax = P.LogSoftmax(axis=-1)
- self.dtype = config.dtype
- self.num_labels = num_labels
- self.dense_1 = nn.Dense(config.hidden_size, self.num_labels, weight_init=self.weight_init,
- has_bias=True).to_float(config.compute_type)
- self.dropout = nn.Dropout(1 - dropout_prob)
- self.reshape = P.Reshape()
- self.shape = (-1, config.hidden_size)
- self.use_crf = use_crf
- self.origin_shape = (-1, config.seq_length, self.num_labels)
-
- def construct(self, input_ids, input_mask, token_type_id):
- """Return the final logits as the results of log_softmax."""
- sequence_output, _, _ = \
- self.bert(input_ids, token_type_id, input_mask)
- seq = self.dropout(sequence_output)
- seq = self.reshape(seq, self.shape)
- logits = self.dense_1(seq)
- logits = self.cast(logits, self.dtype)
- if self.use_crf:
- return_value = self.reshape(logits, self.origin_shape)
- else:
- return_value = self.log_softmax(logits)
- return return_value
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