Johnsnowlabs
Gain access to thejohnsnowlabs ecosystem of enterprise NLP librarieswith over 21.000 enterprise NLP models in over 200 languages with the open sourcejohnsnowlabs library.For all 24.000+ models, see theJohn 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 eithergpu,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 withnlp.load and spark session is started withnlp.start() under the hood.