brightmart / sentiment_analysis_fine_grain Goto Github PK

Hi

Great work!

Can I ask what f1 scores and performance did you get from running run_classifier_multi_labels_bert.py?

Thanks

在 bert_model.py 中第92行，
encoder_class = Encoder(self.d_model, self.d_k, self.d_v, self.sequence_length, self.h, self.batch_size,
self.num_layer, self.input_representation, self.input_representation,
dropout_keep_prob=self.dropout_keep_prob,
use_residual_conn=self.use_residual_conn)
参数mask为何没有赋值，意思是默认不用掩模？但编码器中掩模操作是必须的吧。

在 multi_head_attention.py中第82行，
mask = tf.expand_dims(self.mask, axis=-1) # [batch,sequence_length,1]
mask = tf.expand_dims(mask, axis=1) # [batch,1,sequence_length,1]
dot_product = dot_product + mask # [batch,h,sequence_length,1]

掩模操作怎么会是直接相加呢？

Thank you for a nice example, but I bumped into the following error when executing "estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)" in run_classifier_multi_labels_bert.py.

RuntimeError: Attempted to use a closed Session.

Any suggestion?

target lable: [0, 7, 9, 12, 18, 20, 24, 30, 32, 38, 40, 44, 48, 52, 58, 62, 64, 68, 75, 76]
predict lable: [0, 4, 8, 12, 20, 24, 32, 48, 52, 64, 68]
预测时长度小于二十的怎么恢复到20？

我看了一下代码逻辑，由split后的probabilities和labels来计算accuracy, 下面这个代码块中加粗部分是否应该改成label_ids_split=tf.split(label_ids,FLAGS.num_aspects,axis=-1)？ 这个是否与其他人po的eval_accuracy出错有关？

`def metric_fn(per_example_loss, label_ids, logits):
#print("###metric_fn.logits:",logits.shape) # (?,80)
#predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
#print("###metric_fn.label_ids:",label_ids.shape,";predictions:",predictions.shape) # label_ids: (?,80);predictions:(?,)
logits_split=tf.split(logits,FLAGS.num_aspects,axis=-1) # a list. length is num_aspects
label_ids_split=tf.split(logits,FLAGS.num_aspects,axis=-1) # a list. length is num_aspects
accuracy=tf.constant(0.0,dtype=tf.float64)

    for j,logits in enumerate(logits_split): #
        #  accuracy = tf.metrics.accuracy(label_ids, predictions)

        predictions=tf.argmax(logits, axis=-1, output_type=tf.int32) # should be [batch_size,]
        label_id_=tf.cast(tf.argmax(label_ids_split[j],axis=-1),dtype=tf.int32)
        print("label_ids_split[j]:",label_ids_split[j],";predictions:",predictions,";label_id_:",label_id_)
        current_accuracy,update_op_accuracy=tf.metrics.accuracy(label_id_,predictions)
        accuracy+=tf.cast(current_accuracy,dtype=tf.float64)
    accuracy=accuracy/tf.constant(FLAGS.num_aspects,dtype=tf.float64)
    loss = tf.metrics.mean(per_example_loss)
    return {
        "eval_accuracy": (accuracy,update_op_accuracy),
        "eval_loss": loss,
    }`

https://pan.baidu.com/s/1ZS4dAdOIAe3DaHiwCDrLKw
这里面没有testb测试集，另外testa测试集没有打标呢

我把中文的预训练bert换成英文的bert，就出现了下面的错误，是需要修改什么地方吗？谢谢~
ERROR:tensorflow:Error recorded from evaluation_loop: Restoring from checkpoint failed. This is most likely due to a mismatch between the current graph and the graph from the checkpoint. Please ensure that you have not altered the graph expected based on the checkpoint. Original error:

https://pan.baidu.com/s/1ZS4dAdOIAe3DaHiwCDrLKw
这里面没有testb测试集，另外testa测试集没有打标呢

。。

eval_accuracy = 0.05
eval_loss = 0.1677554
global_step = 78750
loss = 0.16775554