I’m new on Rasa. I tried do built my first NLU-Project for my BA-Thesis. Now, I have a problem where I need your help.
I want to built an application, who classify mailings and extract some data. I produce training examples: 100.000 samples with 6 distinct intents and 3 distinct entities.
Now i try to train the NLU. I use 80% from my Data (20% later with same result). After some time the training aborts with Message “Killed”. On the Web i read, i could be possible that there is not enough Memory.
I have 50GB RAM and some space for swapping (On my local maschine I have 8GB and it returns MemoryError)
I try to configure a smaller batchsize. Put i’m not sure, which Policy will by used.
I use “rasa train --augmentation 0 nlu” to train.
My Config-File looks like:
# Configuration for Rasa NLU. # https://rasa.com/docs/rasa/nlu/components/ language: de pipeline: - name: "WhitespaceTokenizer" - name: "RegexFeaturizer" - name: "CRFEntityExtractor" - name: "EntitySynonymMapper" - name: "CountVectorsFeaturizer" - name: "EmbeddingIntentClassifier" # Configuration for Rasa Core. # https://rasa.com/docs/rasa/core/policies/ policies: # - name: MemoizationPolicy # max_history: 1 - name: KerasPolicy max_history: 1 - name: MappingPolicy - name: EmbeddingPolicy
I try to configure Batchsize on Keras and EmbeddingPolicy in the config file. It didn’t work, so i tried to edit the keras_policy.py and embedding_policy.py directly. I’m not sure, which policy used firstly.
Why my Training aborts with killed. And how I config rasa, that it runs? I use the current Rasa (1.1.4)
I look forward to hearing from you.