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Johnsnowlabs

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

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.

Embed Query with CPU:​

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

Embed Query with GPU:​

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

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)

Embed Query with AARCH:​

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

Embed Document with CPU:​

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

Embed Document with GPU:​

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

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)

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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