- 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:- Create a
.envfile 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,xyzAny model designed for above tasks and compatible with huggingface transformers should work.
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:https://localhost:8000/docs
Notice: This project was initally created to be used in-house, as such the development is first and foremost aligned with the internal requirements.