I am currently using pipeline something like this: pipeline: - name: HFTransformersNLP model_name: "bert" model_weights: "rasa/LaBSE" cache_dir: /tmp - name: LanguageModelFeaturizer model_name: "bert" model_weights: "rasa/LaBSE" cache_dir: /tmp alias: LMF - name: "LanguageModelTokenizer" "intent_tokenization_flag": False "intent_split_symbol": "_" - name: RegexFeaturizer - name: CountVectorsFeaturizer alias: CVF analyzer: char_wb min_ngram: 1 max_ngram: 4 "use_shared_vocab": True - name: DIETClassifier batch_strategy: balanced intent_split_symbol: + intent_tokenization_flag: True epochs: 300 batch_size: 50 - name: CRFEntityExtractor - name: EntitySynonymMapper - name: ResponseSelector featurizers: {CVF, LMF} epochs: 300 retrieval_intent: faq - name: ResponseSelector featurizers: {CVF, LMF} epochs: 300 retrieval_intent: chitchat - name: FallbackClassifier threshold: 0.4 ambiguity_threshold: 0.1 Is it mandatory to use: model_weights: "rasa/LaBSE" or can I cherry-pick from: https://huggingface.co/models for example: model_weights: "bert-large-uncased" and which one would be better to use? Thanks

Hi @mfkarch ! You’re not constrained to work with model_weights: "rasa/LaBSE", that’s just the default value for bert. You can specify the model_weights you’d like to use in your configuration. The options are listed here . As for which one is better to use – that depends on your data! "rasa/LaBSE", for example are language-agnostic embeddings. This may help your model generalize. You can try out a couple different options and see how they affect your model’s performance, but it’s worth noting that there are probably other factors that will have a greater impact on your model’s performance. Check out one of our Algorithm Whiteboard videos on the topic here !

Clarification on Model Weights

Getting Started with Rasa

mfkarch (Faran Khalid) November 23, 2020, 10:00pm 3

Thanks! I’ll surely look into other model weights as well. Since you are here can you answer this as well it’s kind of important and I am stuck there, not at all familiar with the deployment stuff.

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Clarification on Model Weights

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