## Customize which components get used in a request I’m interested in using the same NLU model for a number of tasks, and want to be able to inform certain components they can be skipped in certain contexts. An example would be an NLU pipeline that has: - tokenizer - featurizer - regex entity extractor - crf entity extractor - intent classification --- ## New Training Data Format Ideas What would this mean for json formatted files? --- ## Voice assistance Very cool! --- ## Custom LM with HFTransformers I’m trying to use my own fine-tuned HFTransformers model weights in a pipeline. I’m guessing that it’s possible to do the following, but I’d love input or thoughts on if there’s an easier way: - build an initial fake pipeline using a specific set of HF Transformer model+model_weights combo (say dist…) --- ## Using types when working with rasa from another module Looking into this more, I guess I was asking if rasa was intended to support typing. I don’t see a py.typed even though it looked like much of the codebase was typed. --- ## Word embeddings and RASA NLU This works slightly more simply than that post now. Once you convert the fastText model to spacy vectors, you can just add text_dense_features under CRFEntityExtractor's features, and your SpacyFeaturizer will add a dense vector per token. --- ## Docker-compose not getting "server is running at" message Following that demo, I was able to set the password. I was surprised that simply starting a rasa-x container worked well for me alone, but when started in a docker-compose setting, it didn’t. Thanks for your help! --- ## Easiest way to finetune word vectors Thanks for the reply! That’s not quite what I meant (although an EmbeddingInitializer is an interesting feature idea), here’s what is different. I don’t mean fine-tuning on my labeled, rasa training data. I mean pretraining on some larger corpus of my own. So my goal would be to start with say en_... --- ## Feedback on ConveRT Model + Rasa NLU Convert was really cool off the shelf for chitchat/smalltalk. I haven’t tried it at a featurizer yet, but it worked great out-of-the-box as a response selector. I’d love it if like spacy you could get multiple things out of it. --- ## Rasa X interface for labeling only For anyone interesting in replicating, the only way I found was to create a minimal nlu.md containing one of each entity and one of each intent from your dataset. Put it in data/nlu.md... --- ## What controls available entities/intents for labeling in rasa x? Very cool! Can’t wait to see them. My short term fix has been the following: create a minimum set of training data that hits every intent/entity I want to annotate... --- ## Multiple spacy models in one pipeline Hi there, I’d like to have two spacy models in my pipeline, but the current implementation doesn’t seem well-suited to that. The SpacyNLP object will set "spacy_doc"... --- ## Pass custom features to CRFEntityExtractor A thing that seems like it would add a lot of out-of-the-box power to custom entity recognizers is the ability to pass token-level features to CRFEntityExtractor (previously ner_crf)...