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89 lines
3.6 KiB
Markdown
89 lines
3.6 KiB
Markdown
# NLP - Natural Language Processing
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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.
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## Software used in this pipeline implementation
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- Official Debian Docker image (buster-slim) and programs from its free repositories: https://hub.docker.com/_/debian
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- pyFlow (1.1.20): https://github.com/Illumina/pyflow/releases/tag/v1.1.20
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- spaCy (3.0.3): https://github.com/tesseract-ocr/tesseract/releases/tag/4.1.1
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- spaCy medium sized models (3.0.0):
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- https://github.com/explosion/spacy-models/releases/tag/da_core_news_md-3.0.0
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- https://github.com/explosion/spacy-models/releases/tag/de_core_news_md-3.0.0
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- https://github.com/explosion/spacy-models/releases/tag/el_core_news_md-3.0.0
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- https://github.com/explosion/spacy-models/releases/tag/en_core_web_md-3.0.0
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- https://github.com/explosion/spacy-models/releases/tag/es_core_news_md-3.0.0
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- https://github.com/explosion/spacy-models/releases/tag/fr_core_news_md-3.0.0
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- https://github.com/explosion/spacy-models/releases/tag/it_core_news_md-3.0.0
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- https://github.com/explosion/spacy-models/releases/tag/nl_core_news_md-3.0.0
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- https://github.com/explosion/spacy-models/releases/tag/pt_core_news_md-3.0.0
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- https://github.com/explosion/spacy-models/releases/tag/ru_core_news_md-3.0.0
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- https://github.com/explosion/spacy-models/releases/tag/zh_core_web_md-3.0.0
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## Use this image
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1. Create input and output directories for the pipeline.
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``` bash
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mkdir -p /<my_data_location>/input /<my_data_location>/output
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```
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2. Place your text files inside `/<my_data_location>/input`. Files should all contain text of the same language.
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3. Start the pipeline process. Check the [Pipeline arguments](#pipeline-arguments) section for more details.
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```
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# Option one: Use the wrapper script
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## 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}
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cd /<my_data_location>
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nlp -i input -l <language_code> -o output <optional_pipeline_arguments>
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# Option two: Classic Docker style
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docker run \
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--rm \
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-it \
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-u $(id -u $USER):$(id -g $USER) \
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-v /<my_data_location>/input:/input \
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-v /<my_data_location>/output:/output \
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gitlab.ub.uni-bielefeld.de:4567/sfb1288inf/nlp:1.0.0 \
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-i /input \
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-l <language_code>
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-o /output \
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<optional_pipeline_arguments>
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```
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4. Check your results in the `/<my_data_location>/output` directory.
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```
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### Pipeline arguments
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`--check-encoding`
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* If set, the pipeline tries to automatically determine the right encoding for
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your texts. Only use it if you are not sure that your input is provided in UTF-8.
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* default = False
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* required = False
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`-l languagecode`
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* Tells spaCy which language will be used.
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* options = da (Danish), de (German), el (Greek), en (English), es (Spanish), fr (French), it (Italian), nl (Dutch), pt (Portuguese), ru (Russian), zh (Chinese)
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* required = True
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`--nCores corenumber`
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* Sets the number of CPU cores being used during the NLP process.
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* default = min(4, multiprocessing.cpu_count())
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* required = False
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``` bash
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# Example with all arguments used
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docker run \
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--rm \
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-it \
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-u $(id -u $USER):$(id -g $USER) \
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-v "$HOME"/ocr/input:/input \
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-v "$HOME"/ocr/output:/output \
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gitlab.ub.uni-bielefeld.de:4567/sfb1288inf/nlp:1.0.0 \
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-i /input \
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-l en \
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-o /output \
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--check-encoding \
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--nCores 8 \
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```
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