Explain FastText model using SHAP values

I have trained fastText model and some fully connected network build on its embeddings. I figured out how to use Lime on it: complete example can be found in Natural Language Processing Is Fun Part 3: Explaining Model Predictions

The idea is clear - put 1 sentence into Lime, it drop words and generate some new sentences from my and check how score changes.

My next idea - use SHAP values for this. SHAP values can be used for any deep model, using DeepExplainer. Here is a usage example: Keras LSTM for IMDB Sentiment Classification

But I can't use it for my ensemble, because DeepExplainer needs tensors as input, but I want to fed sentences. I don't want to use BoW or TF-IDF for that - I loose fastText power in that situation. What I want to achieve - get some shap-plots built on words of my sentence. Is it possible?