turn natural language into structured data(支持中文,自定义了N种模型,支持不同的场景和任务)
Rasa NLU (Natural Language Understanding) 是一个自然语义理解的工具,举个官网的例子如下:
"I'm looking for a Mexican restaurant in the center of town"
And returning structured data like:
intent: search_restaurant
entities:
- cuisine : Mexican
- location : center
原来的项目在分支0.2.7上,可自由切换。这个版本的修改是基于最新版本的rasa,将原来rasa_nlu_gao里面的component修改了下,并没有做新增。并且之前做法有些累赘,并不需要在rasa源码中修改。可以直接将原来的component当做addon加载,继承最新版本的rasa,可实时更新。
目前新增的特性如下(请下载最新的rasa-nlu-gao版本)(edit at 2019.06.24):
language: "zh"
pipeline:
- name: "JiebaTokenizer"
- name: "CountVectorsFeaturizer"
token_pattern: "(?u)\b\w+\b"
- name: "EmbeddingIntentClassifier"
- name: "rasa_nlu_gao.extractors.bilstm_crf_entity_extractor.BilstmCRFEntityExtractor"
lr: 0.001
char_dim: 100
lstm_dim: 100
batches_per_epoch: 10
seg_dim: 20
num_segs: 4
batch_size: 200
tag_schema: "iobes"
model_type: "bilstm" # 模型支持两种idcnn膨胀卷积模型或bilstm双向lstm模型
clip: 5
optimizer: "adam"
dropout_keep: 0.5
steps_check: 100
language: "zh"
pipeline:
- name: "JiebaTokenizer"
- name: "CRFEntityExtractor"
- name: "rasa_nlu_gao.extractors.jieba_pseg_extractor.JiebaPsegExtractor"
part_of_speech: ["nr", "ns", "nt"]
- name: "CountVectorsFeaturizer"
OOV_token: oov
token_pattern: "(?u)\b\w+\b"
- name: "EmbeddingIntentClassifier"
language: "zh"
pipeline:
- name: "JiebaTokenizer"
- name: "CRFEntityExtractor"
- name: "JiebaPsegExtractor"
- name: "CountVectorsFeaturizer"
OOV_token: oov
token_pattern: '(?u)\b\w+\b'
- name: "EmbeddingIntentClassifier"
- name: "rasa_nlu_gao.classifiers.entity_edit_intent.EntityEditIntent"
entity: ["nr"]
intent: ["enter_data"]
min_confidence: 0
language: "zh"
pipeline:
- name: "JiebaTokenizer"
- name: "rasa_nlu_gao.featurizers.bert_vectors_featurizer.BertVectorsFeaturizer"
ip: '127.0.0.1'
port: 5555
port_out: 5556
show_server_config: True
timeout: 10000
- name: "EmbeddingIntentClassifier"
- name: "CRFEntityExtractor"
EmbeddingIntentClassifier
和ner_bilstm_crf
这两个使用到tensorflow的组件,配置如下(当然config_proto可以不配置,默认值会将资源全部利用): language: "zh"
pipeline:
- name: "JiebaTokenizer"
- name: "CountVectorsFeaturizer"
token_pattern: '(?u)\b\w+\b'
- name: "EmbeddingIntentClassifier"
config_proto: {
"device_count": 4,
"inter_op_parallelism_threads": 0,
"intra_op_parallelism_threads": 0,
"allow_growth": True
}
- name: "rasa_nlu_gao.extractors.bilstm_crf_entity_extractor.BilstmCRFEntityExtractor"
config_proto: {
"device_count": 4,
"inter_op_parallelism_threads": 0,
"intra_op_parallelism_threads": 0,
"allow_growth": True
}
embedding_bert_intent_classifier
分类器,对应的配置文件如下: language: "zh"
pipeline:
- name: "JiebaTokenizer"
- name: "rasa_nlu_gao.featurizers.bert_vectors_featurizer.BertVectorsFeaturizer"
ip: '127.0.0.1'
port: 5555
port_out: 5556
show_server_config: True
timeout: 10000
- name: "rasa_nlu_gao.classifiers.embedding_bert_intent_classifier.EmbeddingBertIntentClassifier"
- name: "CRFEntityExtractor"
intent_estimator_classifier_tensorflow_embedding_bert
分类器,对应的配置文件如下:language: "zh"
pipeline:
- name: "JiebaTokenizer"
- name: "rasa_nlu_gao.featurizers.bert_vectors_featurizer.BertVectorsFeaturizer"
ip: '127.0.0.1'
port: 5555
port_out: 5556
show_server_config: True
timeout: 10000
- name: "rasa_nlu_gao.classifiers.embedding_bert_intent_estimator_classifier.EmbeddingBertIntentEstimatorClassifier"
- name: "SpacyNLP"
- name: "CRFEntityExtractor"
pip install rasa-nlu-gao
具体的例子请看rasa_chatbot_cn