Natural Language Processing.

Stack:

• FastAPI
• Python
• Docker

Installation

• For ease of use it's recommended to use the provided docker-compose.yml.

CPU Support: Use the latest tag.

services:
  natural_language_processing:
    image: ghcr.io/doppeltilde/natural_language_processing:latest
    ports:
      - "8000:8000"
    volumes:
      - models:/root/.cache/huggingface/hub:rw
    environment:
      - DEFAULT_SUMMARIZATION_MODEL_NAME
      - DEFAULT_TRANSLATION_MODEL_NAME
      - DEFAULT_TEXT_CLASSIFICATION_MODEL_NAME
      - ACCESS_TOKEN
      - USE_API_KEYS
      - API_KEYS
    restart: unless-stopped

volumes:
  models:

NVIDIA GPU Support: Use the latest-cuda tag.

services:
  natural_language_processing_cuda:
    image: ghcr.io/doppeltilde/natural_language_processing:latest-cuda
    ports:
      - "8000:8000"
    volumes:
      - models:/root/.cache/huggingface/hub:rw
    environment:
      - DEFAULT_SUMMARIZATION_MODEL_NAME
      - DEFAULT_TRANSLATION_MODEL_NAME
      - DEFAULT_TEXT_CLASSIFICATION_MODEL_NAME
      - ACCESS_TOKEN
      - DEFAULT_SCORE
      - USE_API_KEYS
      - API_KEYS
    restart: unless-stopped
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all
              capabilities: [ gpu ]

volumes:
  models:

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Environment Variables

• Create a .env file and set the preferred values.

DEFAULT_SUMMARIZATION_MODEL_NAME=Falconsai/text_summarization
DEFAULT_TRANSLATION_MODEL_NAME=google-t5/t5-base
DEFAULT_TEXT_CLASSIFICATION_MODEL_NAME=s-nlp/roberta_toxicity_classifier
ACCESS_TOKEN=

# False == Public Access
# True == Access Only with API Key
USE_API_KEYS=False

# Comma seperated api keys
API_KEYS=abc,123,xyz

Supported NLP tasks

• Summarization
• Translation
• Text Classification

Models

Any model designed for above tasks and compatible with huggingface transformers should work.

Usage

Note

Please be aware that the initial process may require some time, as the model is being downloaded.

Tip

Interactive API documentation can be found at: http://localhost:8000/docs

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Notice: This project was initally created to be used in-house, as such the development is first and foremost aligned with the internal requirements.