您好！

我这边下载了Chinese_roformer-sim-char-ft_L12_H-768_A-12这个模型的权重，但是在加载时，vocab.txt会一直说无法用utf-8编码load。我对比了一下simbert1的vocab文件好像没有这种情况。

我直接读取simbert1的vocab文件作为替代进行生成时，基本都是生僻字（看上去就很像乱码）组成的读不通的句子。。可能是我训练不充分，也有可能是词表不一样

这里有没有办法重新提供一下utf-8编码的Chinese_roformer-sim-char-ft_L12_H-768_A-12的vocab.txt，我不确定simbert1的词表跟simbert2一模一样

万分感谢！

感谢贵公司的开源！ 我在gpu上运行generate.py时报以下错误。

2022-01-18 20:50:55.799119: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfuld dynamic library libcublas.so.10.0 2022-01-18 20:52:03.633057: E tensorflow/stream_executor/cuda/cuda_blas.cc:428] failed to run cuBLAS routine: CUBLAS_STATUS_EXECUTION_FAILED 2022-01-18 20:52:03.633118: E tensorflow/stream_executor/cuda/cuda_blas.cc:2301] Internal: failed BLAS call, see log for details File "/usr/local/miniconda3/lib/python3.6/site-packages/bert4keras/snippets.py", line 627, in random_sample inputs, output_ids, states, temperature, 'probas' File "/usr/local/miniconda3/lib/python3.6/site-packages/bert4keras/snippets.py", line 525, in new_predict prediction = predict(self, inputs, output_ids, states) File "example_generate.py", line 52, in predict return self.last_token(seq2seq).predict([token_ids, segment_ids]) File "/usr/local/miniconda3/lib/python3.6/site-packages/keras/engine/training.py", line 1462, in predict callbacks=callbacks) File "/usr/local/miniconda3/lib/python3.6/site-packages/keras/engine/training_arrays.py", line 324, in predict_loop batch_outs = f(ins_batch) File "/usr/local/miniconda3/lib/python3.6/site-packages/tensorflow/python/keras/backend.py", line 3292, in call run_metadata=self.run_metadata) File "/usr/local/miniconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1458, in call run_metadata_ptr) tensorflow.python.framework.errors_impl.InternalError: 2 root error(s) found. (0) Internal: Blas xGEMMBatched launch failed : a.shape=[12,11,64], b.shape=[12,64,11], m=11, n=11, k=64, batch_size=12 [[{{node Transformer-0-MultiHeadSelfAttention/einsum/MatMul}}]] [[lambda_1/strided_slice/_1229]] (1) Internal: Blas xGEMMBatched launch failed : a.shape=[12,11,64], b.shape=[12,64,11], m=11, n=11, k=64, batch_size=12 [[{{node Transformer-0-MultiHeadSelfAttention/einsum/MatMul}}]] 0 successful operations. 0 derived errors ignored.

google了一下，说是需要添加如下代码：

# 第一种情况
cfg = tf.ConfigProto()
cfg.gpu_options.allow_growth=True
cfg.gpu_options.per_process_gpu_memory_fraction = 0.90
sess = tf.Session(config=cfg)

# 第二种情况
import keras
import tensorflow as tf
 
config = tf.ConfigProto()
config.gpu_options.allow_growth = True      # TensorFlow按需分配显存
config.gpu_options.per_process_gpu_memory_fraction = 0.5  # 指定显存分配比例
keras.backend.tensorflow_backend.set_session(tf.Session(config=config))

我发现我无论是第一种情况还是第二种情况都会报图片中的错误。我用的显卡是A100-SXM4-40GB，安装的是 Package Version

absl-py 0.15.0 asn1crypto 0.24.0 astor 0.8.1 astunparse 1.6.3 bert4keras 0.10.6 cached-property 1.5.2 cachetools 4.2.4 certifi 2018.4.16 cffi 1.11.5 chardet 3.0.4 charset-normalizer 2.0.10 clang 5.0 conda 4.5.4 cryptography 2.2.2 flatbuffers 1.12 gast 0.4.0 google-auth 1.35.0 google-auth-oauthlib 0.4.6 google-pasta 0.2.0 grpcio 1.43.0 h5py 3.1.0 idna 2.6 importlib-metadata 1.6.0 Keras 2.3.1 Keras-Applications 1.0.8 Keras-Preprocessing 1.1.2 Markdown 3.2.2 numpy 1.19.5 oauthlib 3.1.1 opt-einsum 3.3.0 pip 20.1 protobuf 3.12.0 pyasn1 0.4.8 pyasn1-modules 0.2.8 pycosat 0.6.3 pycparser 2.18 pyOpenSSL 18.0.0 PySocks 1.6.8 PyYAML 6.0 requests 2.27.1 requests-oauthlib 1.3.0 rsa 4.8 ruamel-yaml 0.15.37 scipy 1.5.4 setuptools 46.4.0 six 1.15.0 tensorboard 1.14.0 tensorboard-data-server 0.6.1 tensorboard-plugin-wit 1.8.1 tensorflow-estimator 1.14.0 tensorflow-gpu 1.14.0 termcolor 1.1.0 typing-extensions 3.7.4.3 urllib3 1.22 Werkzeug 1.0.1 wheel 0.37.1 wrapt 1.12.1 zipp 3.1.0

我用如下代码测试我的gpu，发现是可以用的。

# 测试gpu是否可以用
import tensorflow as tf
tf.test.is_gpu_available()

请指导一下，如何可以用GPU运行generate.py。 感谢万分，预祝新年快乐~

按照博客里说的“具体来说，Sentence-BERT是将u,v,|u−v|（其中|u−v|是指u−v的每个元素都取绝对值后构成的向量）拼接起来做为特征，后面接一个全连接层做2分类（如果是NLI数据集则是3分类）。” 为什么在train/supervised.py里output = keras.layers.Dense(5, use_bias=False)(output)

我在使用supervised.py代码(https://raw.githubusercontent.com/ZhuiyiTechnology... 微调chinese_roformer-sim-char-ft_L-12_H-768_A-12时，每次跑出来的成绩相差巨大，有时候accuracy能有0.9，有时候0.1都不到，请问您知道是什么原因吗？（数据集是蚂蚁金服的文本匹配数据https://www.kaggle.com/zephyrwang666/afqmc-public） 请老师指点🙏

请问我在16g显存的gpu上测试 roformer-sim，总是会有如下报错：

tensorflow.python.framework.errors_impl.InternalError: Blas xGEMMBatched launch failed : a.shape=[192,17,64], b.shape=[192,17,64], m=17, n=17, k=64, batch_size=192 [[node model_3/Transformer-0-MultiHeadSelfAttention/einsum/Einsum (defined at /anaconda3/envs/nlp_tf/lib/python3.7/site-packages/bert4keras/layers.py:445) ]] [Op:__inference_predict_function_22308]

Errors may have originated from an input operation. Input Source operations connected to node model_3/Transformer-0-MultiHeadSelfAttention/einsum/Einsum: model_3/Transformer-0-MultiHeadSelfAttention/add_1 (defined at /anaconda3/envs/nlp_tf/lib/python3.7/site-packages/bert4keras/layers.py:443) model_3/Transformer-0-MultiHeadSelfAttention/add (defined at /anaconda3/envs/nlp_tf/lib/python3.7/site-packages/bert4keras/layers.py:440)

Function call stack: predict_function

请问这是什么原因呢？