mirror of
https://gitlab.ub.uni-bielefeld.de/sfb1288inf/nlp.git
synced 2024-09-21 11:42:52 +00:00
49 lines
2.2 KiB
Markdown
49 lines
2.2 KiB
Markdown
# NLP - Natural Language Processing
|
|
|
|
This software implements a heavily parallelized pipeline for Natural Language Processing of text files. It is used for nopaque's NLP service but you can also use it standalone, for that purpose a convenient wrapper script is provided.
|
|
|
|
## Software used in this pipeline implementation
|
|
- Official Debian Docker image (buster-slim) and programs from its free repositories: https://hub.docker.com/_/debian
|
|
- pyFlow (1.1.20): https://github.com/Illumina/pyflow/releases/tag/v1.1.20
|
|
- spaCy (3.0.5): https://github.com/tesseract-ocr/tesseract/releases/tag/4.1.1
|
|
- spaCy medium sized models (3.0.0):
|
|
- https://github.com/explosion/spacy-models/releases/tag/de_core_news_md-3.0.0
|
|
- https://github.com/explosion/spacy-models/releases/tag/en_core_web_md-3.0.0
|
|
- https://github.com/explosion/spacy-models/releases/tag/it_core_news_md-3.0.0
|
|
- https://github.com/explosion/spacy-models/releases/tag/nl_core_news_md-3.0.0
|
|
- https://github.com/explosion/spacy-models/releases/tag/pl_core_news_md-3.0.0
|
|
- https://github.com/explosion/spacy-models/releases/tag/zh_core_web_md-3.0.0
|
|
|
|
|
|
## Use this image
|
|
|
|
1. Create input and output directories for the pipeline.
|
|
``` bash
|
|
mkdir -p /<my_data_location>/input /<my_data_location>/output
|
|
```
|
|
|
|
2. Place your text files inside `/<my_data_location>/input`. Files should all contain text of the same language.
|
|
|
|
3. Start the pipeline process. Check the pipeline help (`nlp --help`) for more details.
|
|
```
|
|
# Option one: Use the wrapper script
|
|
## Install the wrapper script (only on first run). Get it from https://gitlab.ub.uni-bielefeld.de/sfb1288inf/nlp/-/raw/1.0.0/wrapper/nlp, make it executeable and add it to your ${PATH}
|
|
cd /<my_data_location>
|
|
nlp -i input -l <language_code> -o output <optional_pipeline_arguments>
|
|
|
|
# Option two: Classic Docker style
|
|
docker run \
|
|
--rm \
|
|
-it \
|
|
-u $(id -u $USER):$(id -g $USER) \
|
|
-v /<my_data_location>/input:/input \
|
|
-v /<my_data_location>/output:/output \
|
|
gitlab.ub.uni-bielefeld.de:4567/sfb1288inf/nlp:1.0.0 \
|
|
-i /input \
|
|
-l <language_code>
|
|
-o /output \
|
|
<optional_pipeline_arguments>
|
|
```
|
|
|
|
4. Check your results in the `/<my_data_location>/output` directory.
|