In this section, we will see how you can deploy your pipeline as a REST API using the power of FastAPI.
Let's create a simple NLP model, that can:
You know the drill:
import edsnlp, edsnlp.pipes as eds
+ Deploying as an API - EDS-NLP Deploying as an API
In this section, we will see how you can deploy your pipeline as a REST API using the power of FastAPI.
The NLP pipeline
Let's create a simple NLP model, that can:
- match synonyms of COVID19
- check for negation, speculation and reported speech.
You know the drill:
pipeline.pyimport edsnlp, edsnlp.pipes as eds
nlp = edsnlp.blank('eds')
nlp.add_pipe(eds.sentences())
@@ -138,4 +138,4 @@
]
}
]
-
\ No newline at end of file
+In this section, we review a few advanced use cases:
In this section, we review a few advanced use cases:
The only ready-to-use components in EDS-NLP are rule-based components. However, that does not prohibit you from exploiting spaCy's machine learning capabilities! You can mix and match machine learning pipelines, trainable or not, with EDS-NLP rule-based components.
In this tutorial, we will explore how you can use static word vectors trained with Gensim within spaCy.
Training the word embedding, however, is outside the scope of this post. You'll find very well designed resources on the subject in Gensim's documenation.
Using Transformer models
spaCy v3 introduced support for Transformer models through their helper library spacy-transformers that interfaces with HuggingFace's transformers library.
Using transformer models can significantly increase your model's performance.
spaCy provides a init vectors CLI utility that takes a Gensim-trained binary and transforms it to a spaCy-readable pipeline.
Using it is straightforward :
$ spacy init vectors fr /path/to/vectors /path/to/pipeline
+ Word embeddings - EDS-NLP Word embeddings
The only ready-to-use components in EDS-NLP are rule-based components. However, that does not prohibit you from exploiting spaCy's machine learning capabilities! You can mix and match machine learning pipelines, trainable or not, with EDS-NLP rule-based components.
In this tutorial, we will explore how you can use static word vectors trained with Gensim within spaCy.
Training the word embedding, however, is outside the scope of this post. You'll find very well designed resources on the subject in Gensim's documenation.
Using Transformer models
spaCy v3 introduced support for Transformer models through their helper library spacy-transformers that interfaces with HuggingFace's transformers library.
Using transformer models can significantly increase your model's performance.
Adding pre-trained word vectors
spaCy provides a init vectors CLI utility that takes a Gensim-trained binary and transforms it to a spaCy-readable pipeline.
Using it is straightforward :
$ spacy init vectors fr /path/to/vectors /path/to/pipeline
---> 100%
color:green Conversion successful!
-
See the documentation for implementation details.
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+See the documentation for implementation details.