[DOCS] Adds NLP with PyTorch section to ML-related page in Eland docs

docs/guide/machine-learning.asciidoc

@@ -1,9 +1,13 @@
 [[machine-learning]]
 == Machine Learning
 
+[discrete]
+[[ml-trained-models]]
+=== Trained models
+
 Eland allows transforming trained models from scikit-learn, XGBoost,
 and LightGBM libraries to be serialized and used as an inference
-model in Elasticsearch
+model in {es}.
 
 [source,python]
 ------------------------
@@ -29,3 +33,48 @@ model in Elasticsearch
 >>> es_model.predict(training_data[0])
 [0 1 1 0 1 0 0 0 1 0]
 ------------------------
+
+[discrete]
+[[ml-nlp-pytorch]]
+=== Natural language processing (NLP) with PyTorch
+
+For NLP tasks, Eland enables you to import PyTorch trained BERT models into {es}. 
+Models can be either plain PyTorch models, or supported 
+https://huggingface.co/transformers[transformers] models from the
+https://huggingface.co/models[Hugging Face model hub].
+
+[source,bash]
+------------------------
+$ eland_import_hub_model \
+  --url http://localhost:9200/ \
+  --hub-model-id elastic/distilbert-base-cased-finetuned-conll03-english \
+  --task-type ner \
+  --start
+------------------------
+
+[source,python]
+------------------------
+>>> import elasticsearch
+>>> from pathlib import Path
+>>> from eland.ml.pytorch import PyTorchModel
+>>> from eland.ml.pytorch.transformers import TransformerModel
+
+# Load a Hugging Face transformers model directly from the model hub
+>>> tm = TransformerModel("elastic/distilbert-base-cased-finetuned-conll03-english", "ner")
+Downloading: 100%|██████████| 257/257 [00:00<00:00, 108kB/s]
+Downloading: 100%|██████████| 954/954 [00:00<00:00, 372kB/s]
+Downloading: 100%|██████████| 208k/208k [00:00<00:00, 668kB/s] 
+Downloading: 100%|██████████| 112/112 [00:00<00:00, 43.9kB/s]
+Downloading: 100%|██████████| 249M/249M [00:23<00:00, 11.2MB/s]
+
+# Export the model in a TorchScrpt representation which Elasticsearch uses
+>>> tmp_path = "models"
+>>> Path(tmp_path).mkdir(parents=True, exist_ok=True)
+>>> model_path, config_path, vocab_path = tm.save(tmp_path)
+
+# Import model into Elasticsearch
+>>> es = elasticsearch.Elasticsearch("http://elastic:mlqa_admin@localhost:9200", timeout=300)  # 5 minute timeout
+>>> ptm = PyTorchModel(es, tm.elasticsearch_model_id())
+>>> ptm.import_model(model_path, config_path, vocab_path)
+100%|██████████| 63/63 [00:12<00:00,  5.02it/s]
+------------------------