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Dockerfile 0.42%
Python 45.21%
Shell 0.27%
Go 54.10%
nlp-dlt's Introduction
- Clone the repository
- Build a docker image:
cd ~/NLP-DLT/nlp
docker build -t nlp-engine .
- To verify the docker image should be used the command
docker images
![](https://github.com/sfl0r3nz05/NLP-DLT/raw/main/dockerVerification.png)
- Create an environmental variables file based on .env.example (~/NLP-DLT/network)
- Obtain access keys from AWS E.g.:
- Update the environment variable
AWS_ACCESS_KEY_ID
- Update the environment variable
AWS_SECRET_ACCESS_KEY
- Update the environment variable
AWS_SESSION_TOKEN
- Update the path of PDF files which contains Roaming Agreements
- Default place:
~/NLP-DLT/nlp/input
- Update the path of JSON files
- Default place:
~/NLP-DLT/nlp/output
cd ~/NLP-DLT/network
- Start:
docker-compose up -d
- Stop:
docker-compose stop
- Down:
docker-compose down
- Fix heading detection when sub-articles are analized.
- Hardcode of the default template.json file.
- Hardcode of list of articles.json file.
- Fix the code to correctly determine the organizations variables.
- Determine the accuracy of the NLP engine to extract variables and variations.
- Complete the documentation of the library.
- Perform the analysis one level down to go on the sentence level:
- There are two branches created:
main
and sentencelvl
- To change to the branch
main
use the command git checkout sentencelvl
- Automate accuracy determination.
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