I've read the documentation on ML and deploying ML models Overview | Machine Learning in the Elastic Stack [8.6] | Elastic.
Does anyone have examples of how to do the following:
- Create a custom ML model that fits the needs (pytorch, TorchScript representation, etc.)
- Deploy an local ML model not hosted on huggingface.co
One example would be to fine tune a text embedding model for your specific needs. Using one of the compatible models listed here as the base model.
Use the Eland
eland_import_hub_model script with the
--hub-model-id argument set to the the path to the model.
See the example in Deploy trained model offline environment · Issue #502 · elastic/eland · GitHub
Thank you for your quick response. The offline example seems feasible. I will test that - thank you very much.
Do you have examples of creating your own ML models that meet Elastic requirements? I have the language requirement to provide NER, fill-mask, Q&A, text classification and embedding and also similarity in German language. So I'm afraid deriving a model from an existing one from Huggingface (most are English) would not be sufficient for our needs.
Thanks and best regards
Creating and training a transformer model is quite an advanced task and beyond the scope of this forum sorry.
There are a number of German language models on HuggingFace, simply searching for German returns 404 models.
Here's a German Bert base: bert-base-german-cased · Hugging Face
A model for sentiment analysis: oliverguhr/german-sentiment-bert · Hugging Face
Q and A: deepset/gbert-base-germandpr-question_encoder · Hugging Face
And many more...
Not all of the models will be compatible with Elastic, in general most BERT based models will be compatible and are a good place to start. See also the list of known compatible models (this is a small sample there will be many more models that are compatible).
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