add documentation on custom embedding models

Author: jstammers

docs/tutorials/pyTorchWorkflow.ipynb

@@ -124,6 +124,10 @@
     "- pyTorch Sentence transformers : `{'smpnet','st5','sdistilroberta','sminilm','sent_glove'}`\n",
     "- pyTorch Word transformers :`{'bert', 'distilbert', 'roberta', 'xlnet', 'albert'}`\n",
     "\n",
+    "Custom Word or Sentence embedding models can be specified using a file path or HuggingFace identifier and a corresponding argument to `emb.build_blocks`\n",
+    "- Custom Sentence transformers: `vectorizer='model_name'` and `emb.build_blocks(..., custom_pretrained_model='sentence')`\n",
+    "- Custom Word transformers: `vectorizer='model_name'` and `emb.build_blocks(..., custom_pretrained_model='word')`\n",
+    "\n",
     "## FAISS\n",
     "\n",
     "faiss.IndexIVFFlat is an implementation of an inverted file index with coarse quantization. This index is used to efficiently search for nearest neighbors of a query vector in a large dataset of vectors. Here's a brief explanation of the parameters used in this index:\n"
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