{"id":18594431,"url":"https://github.com/applenob/rnn-for-joint-nlu","last_synced_at":"2025-04-09T15:06:12.961Z","repository":{"id":78148086,"uuid":"107490629","full_name":"applenob/RNN-for-Joint-NLU","owner":"applenob","description":"Tensorflow implementation of \"Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling\" (https://arxiv.org/abs/1609.01454)","archived":false,"fork":false,"pushed_at":"2018-01-05T01:56:55.000Z","size":412,"stargazers_count":281,"open_issues_count":10,"forks_count":96,"subscribers_count":17,"default_branch":"master","last_synced_at":"2025-04-09T15:06:08.111Z","etag":null,"topics":["nlu","seq2seq","sequence-labeling","slot-filling","tensorflow"],"latest_commit_sha":null,"homepage":null,"language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# RNN-for-Joint-NLU\n\n## 模型介绍\n\n![](https://github.com/applenob/RNN-for-Joint-NLU/raw/master/res/arc.png)\n\n使用tensorflow r1.3 api，Encoder使用`tf.nn.bidirectional_dynamic_rnn`实现，Decoder使用`tf.contrib.seq2seq.CustomHelper`和`tf.contrib.seq2seq.dynamic_decode`实现。\n\n[原作者Bing Liu的Tensorflow实现](https://github.com/HadoopIt/rnn-nlu)\n\n我的实现相对比较简单，用于学习目的。\n\n## 使用\n\n```\npython main.py\n```\n\n输出：\n```\n[Epoch 27] Average train loss: 0.0\nInput Sentence        :  ['what', 'are', 'the', 'flights', 'and', 'prices', 'from', 'la', 'to', 'charlotte', 'for', 'monday', 'morning']\nSlot Truth            :  ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-fromloc.city_name', 'O', 'B-toloc.city_name', 'O', 'B-depart_date.day_name', 'B-depart_time.period_of_day']\nSlot Prediction       :  ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'B-fromloc.city_name', 'O', 'B-toloc.city_name', 'O', 'B-depart_date.day_name', 'B-depart_time.period_of_day']\nIntent Truth          :  atis_flight\nIntent Prediction     :  atis_flight#atis_airfare\nIntent accuracy for epoch 27: 0.969758064516129\nSlot accuracy for epoch 27: 0.9782146713160718\nSlot F1 score for epoch 27: 0.977950943062074\n[Epoch 28] Average train loss: 0.0\nInput Sentence        :  ['show', 'me', 'the', 'last', 'flight', 'from', 'love', 'field']\nSlot Truth            :  ['O', 'O', 'O', 'B-flight_mod', 'O', 'O', 'B-fromloc.airport_name', 'I-fromloc.airport_name']\nSlot Prediction       :  ['O', 'O', 'O', 'B-flight_mod', 'O', 'O', 'B-fromloc.airport_name', 'I-fromloc.airport_name']\nIntent Truth          :  atis_flight\nIntent Prediction     :  atis_flight\nIntent accuracy for epoch 28: 0.9717741935483871\nSlot accuracy for epoch 28: 0.9794670271393975\nSlot F1 score for epoch 28: 0.9792847025495751\n```\n\n## 细节\n\n博客文章：\n- [Tensorflow动态seq2seq使用总结（r1.3）](https://github.com/applenob/RNN-for-Joint-NLU/blob/master/tensorflow_dynamic_seq2seq.md)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fapplenob%2Frnn-for-joint-nlu","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fapplenob%2Frnn-for-joint-nlu","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fapplenob%2Frnn-for-joint-nlu/lists"}