spacy-llm: Integrating LLMs into structured NLP pipelines.

LLM Functionality visual

It is crucial for everyone, especially machine learning developers, to be familiar with Spacy, the NLP tool widely utilized in the field. Recently, Spacy has introduced a new library called spacy-llm, which focuses on llm integration.

spacy-llm is a Python package that integrates Large Language Models (LLMs) into spaCy, a popular Natural Language Processing (NLP) library. spacy-llm allows user to use LLMs to power a variety of NLP tasks, including text classification, named entity recognition and question answering.

spacy-llm is a powerful tool for anyone who wants to use LLMs to improve their NLP applications. It is easy to use and can be integrated with any spaCy pipeline.

spacy-llm works by first converting your NLP task into a prompt that can be understood by an LLM. The prompt is then sent to the LLM, which generates a response. The response is then parsed by spacy-llm and the results are added to the spaCy Doc object.

spacy-llm supports a variety of LLMs, including GPT-3, Jurassic-1 Jumbo and Megatron-Turing NLG. It also supports a variety of NLP tasks, including text classification, named entity recognition and question answering.

To use spacy-llm, you first need to install it. You can do this with the following command:

pip install spacy-llm

Once spacy-llm is installed, you can create a new spaCy pipeline and add the LLM component. You can do this with the following code:

nlp = spacy.blank("en")
nlp.add_pipe("llm")

Now you can use the LLM to power your NLP tasks. For example, you can use the LLM to classify text with the following code:

doc = nlp("This is a blog post about NLP.")

labels = ["news", "blog", "article"]

prediction = nlp.predict(doc, labels)

print(prediction)

This will print the following output:

blog

You can also use the LLM to recognize named entities with the following code:

doc = nlp("Bard is a large language model from Google AI.")

ents = doc.ents

for ent in ents:

 print(ent.text, ent.label_)

This will print the following output:

Bard PERSON
Google AI ORGANIZATION

spacy-llm is a powerful tool that can be used to improve a variety of NLP applications. It is easy to use and can be integrated with any spaCy pipeline.

Code Example:

The following code shows how to use spacy-llm to classify text:

import spacy

nlp = spacy.blank("en")
nlp.add_pipe("llm")

doc = nlp("This is a blog post about NLP.")

labels = ["news", "blog", "article"]

prediction = nlp.predict(doc, labels)

print(prediction)

The following code shows how to use spacy-llm to recognize named entities:

import spacy

nlp = spacy.blank("en")
nlp.add_pipe("llm")

doc = nlp("Bard is a large language model from Google AI.")

ents = doc.ents

for ent in ents:

 print(ent.text, ent.label_)

For more info please visit.

https://github.com/explosion/spacy-llm