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Gain access to the johnsnowlabs ecosystem of enterprise NLP libraries with over 21.000 enterprise NLP models in over 200 languages with the open source johnsnowlabs library. For all 24.000+ models, see the John Snow Labs Model Models Hub

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Installation and setup

pip install johnsnowlabs
To [install enterprise features](https://nlp.johnsnowlabs.com/docs/en/jsl/install_licensed_quick, run: 
# for more details see https://nlp.johnsnowlabs.com/docs/en/jsl/install_licensed_quick
nlp.install()
You can embed your queries and documents with either gpu,cpu,apple_silicon,aarch based optimized binaries. By default cpu binaries are used. Once a session is started, you must restart your notebook to switch between GPU or CPU, or changes will not take effect.

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Embed query with CPU:

document = "foo bar"
embedding = JohnSnowLabsEmbeddings('embed_sentence.bert')
output = embedding.embed_query(document)

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Embed query with GPU:

document = "foo bar"
embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','gpu')
output = embedding.embed_query(document)

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Embed query with apple silicon (M1,M2,etc..):

documents = ["foo bar", 'bar foo']
embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','apple_silicon')
output = embedding.embed_query(document)

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Embed query with AARCH:

documents = ["foo bar", 'bar foo']
embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','aarch')
output = embedding.embed_query(document)

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Embed document with CPU:

documents = ["foo bar", 'bar foo']
embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','gpu')
output = embedding.embed_documents(documents)

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Embed document with GPU:

documents = ["foo bar", 'bar foo']
embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','gpu')
output = embedding.embed_documents(documents)

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Embed document with apple silicon (M1,M2,etc..):

```python
documents = ["foo bar", 'bar foo']
embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','apple_silicon')
output = embedding.embed_documents(documents)

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Embed Document with AARCH:

```python
documents = ["foo bar", 'bar foo']
embedding = JohnSnowLabsEmbeddings('embed_sentence.bert','aarch')
output = embedding.embed_documents(documents)
Models are loaded with nlp.load and spark session is started with nlp.start() under the hood.
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