您好, 我照着train.py中的代码使用在finetune.py, 在训练时发生以下错误, 请问我要怎么修改, 才能正确训练? 谢谢

InvalidArgumentError: 2 root error(s) found. (0) Invalid argument: TypeError: generator yielded an element that could not be converted to the expected type. The expected type was float32, but the yielded element was [array([[ 101, 2349, 25480, ..., 9172, 16054, 102], [ 101, 2335, 5088, ..., 4934, 31621, 102], [ 101, 2349, 25480, ..., 18312, 5661, 102], [ 101, 2349, 25480, ..., 33732, 11511, 102]]), array([[ 101, 22191, 27209, 41412, 31201, 8506, 42696, 31201, 5661,

苏神你好，首先感谢大佬分享！

我最近在使用bert4keras做实体识别任务，具体脚本用的是： https://github.com/bojone/bert4keras/blob/master/examples/task_sequence_labeling_ner_crf.py

python3.6.9 tensorflow-gpu 1.14.0 keras 2.3.1 bert4keras 0.10.7

想尝试通过该脚本用t5模型，但是在加载预训练模型chinese_t5_pegasus_base是报如下错误：

Instructions for updating: Use tf.where in 2.0, which has the same broadcast rule as np.where Traceback (most recent call last): File "task_sequence_labeling_ner_crf_conlleval.py", line 155, in model = build_transformer_model(config_path, checkpoint_path, model=is_albert) File "/big_disk/ner_2/bert4keras/models.py", line 2451, in build_transformer_model transformer.load_weights_from_checkpoint(checkpoint_path) File "/big_disk/ner_2/bert4keras/models.py", line 305, in load_weights_from_checkpoint raise e File "/big_disk/ner_2/bert4keras/models.py", line 299, in load_weights_from_checkpoint values.append(self.load_variable(checkpoint, v)) File "/big_disk/ner_2/bert4keras/models.py", line 1763, in load_variable variable = super(T5_Base, self).load_variable(checkpoint, name) File "/big_disk/ner_2/bert4keras/models.py", line 270, in load_variable return tf.train.load_variable(checkpoint, name) File "/big_disk/venv36/lib/python3.6/site-packages/tensorflow/python/training/checkpoint_utils.py", line 84, in load_variable return reader.get_tensor(name) File "/big_disk/venv36/lib/python3.6/site-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 678, in get_tensor return CheckpointReader_GetTensor(self, compat.as_bytes(tensor_str)) tensorflow.python.framework.errors_impl.NotFoundError: Key encoder/final_layer_norm/scale not found in checkpoint

请问可以用该脚本使用t5模型做实体识别任务吗？应该如何解决加载预训练模型chinese_t5_pegasus_base出现的错误呢？

再次感谢大佬！

错误: tensorflow.python.framework.errors_impl.DataLossError: Checksum does not match: stored 592068290 vs. calculated on the restored bytes 517592881 网盘下载的权重, 使用google mt5没有问题 gpu v100 32g

查了下问题,没查到.到load_variable函数的时候报的错,不知道是不是的t5-pegasus问题.

你好，在finetune.py文件中提示缺少self.last_token函数

class AutoTitle(AutoRegressiveDecoder): """seq2seq解码器 """ @AutoRegressiveDecoder.wraps(default_rtype='probas') def predict(self, inputs, output_ids, states): c_encoded = inputs[0] return self.last_token(decoder).predict([c_encoded, output_ids])

参考Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering这篇论文， https://arxiv.org/pdf/2007.01282.pdf 想将您的代码改成输入为多doc，分别经过encoder后，将表示concat起来，送入decoder。如下代码

input_ids = Input(shape=(max_padding_len, max_c_len), name='INPUT_contents_ids', dtype=tf.int32)
input_ans_ids = Input(shape=(max_a_len,), name='INPUT_ans_ids', dtype=tf.int32)
input_ids_reshape = K.reshape(input_ids,(-1, max_c_len))  # (bs*max_padding_len, max_seq_len)

t5 = build_transformer_model(config_path=config_path,checkpoint_path=checkpoint_path,model='t5.1.1',return_keras_model=False,name='T5')
encoder = t5.encoder
decoder = t5.decoder
resp = encoder(input_ids_reshape)  # (bs*max_padding_len, max_c_len, 512)
resp_concat = K.reshape(resp, (-1, max_padding_len * max_c_len, 512))   # (bs, max_padding_len*max_c_len, 512)
out = decoder([resp_concat, input_ans_ids])

output = CrossEntropy(1)([input_ans_ids, out])
model = Model(inputs=[input_ids, input_ans_ids], outputs=output)

会报错 AttributeError: 'NoneType' object has no attribute '_inbound_nodes' 可能是因为decoder是Model属性导致的？ 请问怎么解决呢？可以重新给decoder传入tensor 多谢

您好，运行环境跟您一样，运行train.py的时候，最后到model.fit(dataset,steps_per_epoch=1000, ......)时候报错 AttributeError: 'DatasetV1Adapter' object has no attribute 'ndim'，这里dataset是'DatasetV1Adapter'，这个“ndim”是什么呢？是一个DatasetV1Adapter的属性吗？还是因为其他什么原因呢？

os.environ['TF_KERAS'] = '1' tensorflow 版本为2.1.1 ，keras=2.3.1 model.load_weights('./best_model.weights') 转化为pd文件 keras.models.save_model(encoder, "model_save_path_encoder", save_format='tf') 转化为onnx文件 python -m tf2onnx.convert --saved-model model_save_path_encoder --opset 13 --output ./model.onnx

尝试了tensor.eval()和tensor.numpy()均不行 第一种报错大致意思说缺少一个session，给他新建一个session报错说不在原来session内 第二种方式中 tensorflow.python.framework.ops.EagerTensor 这种类型的可以转换，而代码里的类型为tensorflow.python.framework.ops.Tensor，加上tf.enable_eager_execution()也不行 求解答

config_path = '/root/bert/chinese_t5_pegasus_base/config.json' checkpoint_path = '/root/bert/chinese_t5_pegasus_base/model.ckpt'

build_transformer_model( config_path=config_path, checkpoint_path=checkpoint_path, model='t5.1.1', return_keras_model=False, name='T5', ) 加载方式需要改为model='mt5.1.1'