hellonlp / classifier-multi-label Goto Github PK

多标签文本分类，多标签分类，文本分类, multi-label, classifier, text classification, BERT, seq2seq，attention, multi-label-classification

Python 100.00%

attention bert classifier-multi-label cnn multi-label multi-label-classification seq2seq tensorflow text-classification text-classifier textcnn

classifier-multi-label's Introduction

你好，我是HelloNLP. 👋

🤗 ：huggingface
🌱 ：知乎
👯 ：hellonlp

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classifier-multi-label's Issues

ImportError：cannot import name 'model_fn' from 'tensorflow_estimator.python.estimator'

您好，能分享下最后版本的requirements.txt吗？看了您的知乎文章，tf=1.14.0，但是安装了之后依旧报错，如题。

tf2使用textcnn网络问题

tf1 版本转tf2问题，当不添加textcnn网络时，训练预测均没有问题。但是当加入textcnn时训练时loss与acc都不错，但是预测都是错误的。以下tf2实现的textcnn基本都是直接转的。此外我还尝试tf.keras.layers.Conv2D()以及conv1d实现。但是效果都不行，本来考虑是不是训练周期等参数问题，但是跟您的项目参数保持一致，训练出来的模型就是有问题（有进行dropout)，所以想请教一下您。

def textcnn(x):
    pooled_outputs = []

    filter_sizes = [2, 3, 4, 5, 6, 7]
    inputs_expand = tf.expand_dims(x, -1)
    for filter_size in filter_sizes:
        filter_shape = [filter_size, 312, 1, 128]
        W = tf.Variable(tf.random.truncated_normal(filter_shape, stddev=0.1), dtype=tf.float32, name="W")
        b = tf.Variable(tf.constant(0.1, shape=[128]), dtype=tf.float32, name="b")
        conv = tf.nn.conv2d(
            inputs_expand,
            W,
            strides=[1, 1, 1, 1],
            padding="VALID",
            name="conv")
        # Apply nonlinearity
        h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu")
        # Maxpooling over the outputs
        pooled = tf.nn.max_pool(
            h,
            ksize=[1, 60 - filter_size + 1, 1, 1],
            strides=[1, 1, 1, 1],
            padding='VALID',
            name="pool")
        pooled_outputs.append(pooled)
    # Combine all the pooled features
    num_filters_total = 128 * len(filter_sizes)
    h_pool = tf.concat(pooled_outputs, 3)
    h_pool_flat = tf.reshape(h_pool, [-1, num_filters_total])

    return h_pool_flat

大佬问一下tensor2，怎么解决contrib_tpu的问题

from tensorflow.contrib import tpu as contrib_tpu
ModuleNotFoundError: No module named 'tensorflow.contrib'

The passed save_path is not a valid checkpoin

Traceback (most recent call last):
File "predict.py", line 43, in
MODEL = ModelAlbertTextCNN()
File "predict.py", line 26, in init
self.albert, self.sess = self.load_model()
File "predict.py", line 39, in load_model
saver.restore(sess, ckpt.model_checkpoint_path)
ValueError: The passed save_path is not a valid checkpoint

def get_label(sentence):
    """
    Prediction of the sentence's label.
    """
    feature = get_feature_test(sentence)
    fd = {MODEL.albert.input_ids: [feature[0]],
          MODEL.albert.input_masks: [feature[1]],
          MODEL.albert.segment_ids:[feature[2]],
          }
    prediction = MODEL.sess.run(MODEL.albert.predictions, feed_dict=fd)[0]
    print(prediction)
    r=[]
    for i in range(len(prediction)):
        if prediction[i]!=0.0:
            r.append(id2label(i))
    return r
    #return [id2label(l) for l in np.where(prediction==1)[0] if l!=0]