Comments (6)
hkdafang
commented on August 18, 2024
我也用其他数据跑了一遍，也是不收敛
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momoz44
commented on August 18, 2024
是不是词向量不包含的问题？作者用的词向量是预先用glove的方法训练好的。没有中文就不会生成对应向量吧。
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bobkentt
commented on August 18, 2024
是不是词向量不包含的问题？作者用的词向量是预先用glove的方法训练好的。没有中文就不会生成对应向量吧。
我用word2vec预先训练了词向量可以
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ily666666
commented on August 18, 2024
是不是词向量不包含的问题？作者用的词向量是预先用glove的方法训练好的。没有中文就不会生成对应向量吧。
我用word2vec预先训练了词向量可以
怎么做呀
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Z1hgq
commented on August 18, 2024
是不是词向量不包含的问题？作者用的词向量是预先用glove的方法训练好的。没有中文就不会生成对应向量吧。
我用word2vec预先训练了词向量可以
怎么做呀
根据语料库生成词典，然后词典中每一个单词生成同维度向量
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Alice1222518
commented on August 18, 2024
是不是词向量不包含的问题？作者用的词向量是预先用glove的方法训练好的。没有中文就不会生成对应向量吧。
我用word2vec预先训练了词向量可以
怎么做呀
根据语料库生成词典，然后词典中每一个单词生成同维度向量
请问训练词向量使用的语料是与训练chatbot相同的语料吗
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Related Issues (20)
- any code for tensoflow > 2.0
- How to solve AttributeError: module 'tensorflow.contrib.learn' has no attribute 'estimators' HOT 4
- Derive actual response from the probability? Just wondering how do I generate actual response from this model? HOT 1
- Gettin error while running idc_train.py HOT 3
- How to select candidate answers when predict HOT 1
- How to Deal with Context of multiple column ?
- How can I export/serve this model using saved_model_cli ?
- How to stops training after specied number of steps? HOT 1
- InvalidArgumentError (see above for traceback): indices[24,12] = 135816 is not in [0, 91620)
- InvalidArgumentError: Name: <unknown>, Feature: distractor_1 (data type: int64) is required but could not be found. [[{{node read_batch_features_eval/ParseExample/ParseExample}}]]
- InvalidArgumentError (see above for traceback): indices[7,16] = 99296 is not in [0, 91620)
- ValueError: Shapes (10, ?, 160) and () are incompatible
- Incompatible shapes: [20,1] vs. [80,1] HOT 2
- UnicodeDecodeError: 'gbk' codec can't decode byte 0xbf in position 2: illegal multibyte sequence HOT 1
- The question about Tensorflow about Incompatible shapes: [730,5] vs. [30,5]
- udc_test.py出错
- Data missing from drive
- any examples of chatbot conversation?
- InvalidArgumentError: Incompatible shapes: [128,14,14,16] vs. [8] [[{{node max_unpooling2d_4/max_unpooling2d_4/mul_4}}]] [[{{node Mean_1}}]] HOT 1
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