Closed srimantacse closed 1 year ago
@maziyarpanahi Request to give input on the above query.
Hi,
The fine-tuning of any transformer models must happen outside Spark NLP. The Java APIs for TensorFlow (or any other DL framework) don't have the feature to fine-tune, only Python has.
So you pick a model in HuggingFace, let's say BERT Small (or Tiny, or any size), you will fine-tune it for the next sentence prediction over your own domain-specific dataset (they have examples of how to do NSP and there are many online tutorials), and once you are done you will import that model into BertSentenceEmbeddings
annotator which you can use for training classifiers or any other tasks but at scale with zero-code change when you go from 1 machine to N machines.
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This is not a proper feature request; rather I need the guidance to build our customized model using BertSentenceEmbedding which would be built on top of pretrained model for ex: small_bert_L2_128; I will use some domain specific dataset to finetune the mentioned model. Request to share the approach in spark-nlp perspective.