nlpcloud-tagger

Entity tag, summarize, analyze, generate, and parse text. Just load content into Steamship and apply NLP Cloud's full library of models.

Using

Tagger Plugins add annotations to text that can be queried and composed later.

Blockifiers convert data into Steamship’s native Block format.

Importer Plugins add annotations to text that can be queried and composed later.

Use them when writing Packages to help you work with data of different types.

Links

from steamship import Steamship, File

client = Steamship(workspace="my-workspace-handle")

# Import a file to Steamship
with open("file.ext") as f:
  file = File.create(content=file.read())

# Create an instance of this blockifier
blockifier = client.use_plugin(
  'nlpcloud-tagger'
)

# Blockify the file
task = file.blockify()
task.wait()
from steamship import Steamship, File

client = Steamship(workspace="my-workspace-handle")

# Import a file to Steamship
with open("file.ext") as f:
  file = File.create(content=file.read())

# Create a blockifier. We'll assume Markdown here.
blockifier = client.use_plugin(
  'markdown-blockifier-default'
)

# Blockify the file
task = file.blockify()

# Create an instance of this tagger.
tagger = client.use_plugin(
  'nlpcloud-tagger'
)

# Tag the file
task = file.tag()

task.wait()
Pulled from the GitHub repository.
# NLPCloud Tagger Plugin - Steamship

This project contains a Steamship Tagger plugin that enables use of NLPCloud's text tagging pipeline.

## Configuration

This plugin must be configured with the following fields:

* `api_key`    - Your One AI API key
* `input_type` - Either `conversation` or `article`. [One AI Documentation](https://studio.oneai.com/docs?api=Pipeline+API&item=Expected+Input+Format&accordion=Introduction%2CPipeline+API%2CNode.js+SDK+Reference%2CClustering+API)
* `skills`     - A CSV list of One AI "skills" that produce tags

Example skills are:

* `entities` - Tags real-world objects, such as people, organizations, and time frames.
* `topics` - Tags text with relevant topics.
* `sentiment` - Tags text with phrases containing positive and negative sentiment: Output tags: `[POS, NEG]`
* `emotions` - Tags text with phrases describing emotion: Output tags: `[happiness, sadness, anger, surprise, fear]`
* `highlights` - Tags text with selected highlights: Output tags: `[highlight]`
* `keywords` - Tags text with selected keywords.
* `sentences` - Splits sentences by text.
* `action_items` - Tags text for action items.

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