https://github.com/qurator-spk/sbb_ner
Named Entity Recognition
https://github.com/qurator-spk/sbb_ner
bert-ner named-entity-recognition qurator
Last synced: 6 months ago
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Named Entity Recognition
- Host: GitHub
- URL: https://github.com/qurator-spk/sbb_ner
- Owner: qurator-spk
- License: apache-2.0
- Created: 2019-08-27T11:11:58.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2025-04-04T13:32:05.000Z (about 1 year ago)
- Last Synced: 2025-04-04T13:35:31.517Z (about 1 year ago)
- Topics: bert-ner, named-entity-recognition, qurator
- Language: Python
- Homepage:
- Size: 323 KB
- Stars: 17
- Watchers: 6
- Forks: 2
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
How the models have been obtained is described in our [paper](https://corpora.linguistik.uni-erlangen.de/data/konvens/proceedings/papers/KONVENS2019_paper_4.pdf).
***
# Installation:
Recommended python version is 3.11.
Consider use of [pyenv](https://github.com/pyenv/pyenv) if that python version is not available on your system.
Activate virtual environment (virtualenv):
```
source venv/bin/activate
```
or (pyenv):
```
pyenv activate my-python-3.11-virtualenv
```
Update pip:
```
pip install -U pip
```
Install sbb_ner:
```
pip install git+https://github.com/qurator-spk/sbb_ner.git
```
Download required models: https://qurator-data.de/sbb_ner/models.tar.gz
Extract model archive:
```
tar -xzf models.tar.gz
```
Copy [config file](qurator/sbb_ner/webapp/config.json) into working directory.
Set USE_CUDA environment variable to True/False depending on GPU availability.
Run webapp directly:
```
env CONFIG=config.json env FLASK_APP=qurator/sbb_ner/webapp/app.py env FLASK_ENV=development env USE_CUDA=True/False flask run --host=0.0.0.0
```
For production purposes rather use
```
env CONFIG=config.json env USE_CUDA=True/False gunicorn --bind 0.0.0.0:5000 qurator.sbb_ner.webapp.wsgi:app
```
# Docker
## CPU-only:
```
docker build --build-arg http_proxy=$http_proxy -t qurator/webapp-ner-cpu -f Dockerfile.cpu .
```
```
docker run -ti --rm=true --mount type=bind,source=data/konvens2019,target=/usr/src/qurator-sbb-ner/data/konvens2019 -p 5000:5000 qurator/webapp-ner-cpu
```
## GPU:
Make sure that your GPU is correctly set up and that nvidia-docker has been installed.
```
docker build --build-arg http_proxy=$http_proxy -t qurator/webapp-ner-gpu -f Dockerfile .
```
```
docker run -ti --rm=true --mount type=bind,source=data/konvens2019,target=/usr/src/qurator-sbb-ner/data/konvens2019 -p 5000:5000 qurator/webapp-ner-gpu
```
NER web-interface is availabe at http://localhost:5000 .
# REST - Interface
Get available models:
```
curl http://localhost:5000/models
```
Output:
```
[
{
"default": true,
"id": 1,
"model_dir": "data/konvens2019/build-wd_0.03/bert-all-german-de-finetuned",
"name": "DC-SBB + CONLL + GERMEVAL"
},
{
"default": false,
"id": 2,
"model_dir": "data/konvens2019/build-on-all-german-de-finetuned/bert-sbb-de-finetuned",
"name": "DC-SBB + CONLL + GERMEVAL + SBB"
},
{
"default": false,
"id": 3,
"model_dir": "data/konvens2019/build-wd_0.03/bert-sbb-de-finetuned",
"name": "DC-SBB + SBB"
},
{
"default": false,
"id": 4,
"model_dir": "data/konvens2019/build-wd_0.03/bert-all-german-baseline",
"name": "CONLL + GERMEVAL"
}
]
```
Perform NER using model 1:
```
curl -d '{ "text": "Paris Hilton wohnt im Hilton Paris in Paris." }' -H "Content-Type: application/json" http://localhost:5000/ner/1
```
Output:
```
[
[
{
"prediction": "B-PER",
"word": "Paris"
},
{
"prediction": "I-PER",
"word": "Hilton"
},
{
"prediction": "O",
"word": "wohnt"
},
{
"prediction": "O",
"word": "im"
},
{
"prediction": "B-ORG",
"word": "Hilton"
},
{
"prediction": "I-ORG",
"word": "Paris"
},
{
"prediction": "O",
"word": "in"
},
{
"prediction": "B-LOC",
"word": "Paris"
},
{
"prediction": "O",
"word": "."
}
]
]
```
The JSON above is the expected input format of the
[SBB named entity linking and disambiguation system](https://github.com/qurator-spk/sbb_ned).
# Model-Training
***
## Preprocessing of NER ground-truth:
### compile_conll
Read CONLL 2003 ner ground truth files from directory and
write the outcome of the data parsing to some pandas DataFrame that is
stored as pickle.
#### Usage
```
compile_conll --help
```
### compile_germ_eval
Read germ eval .tsv files from directory and write the
outcome of the data parsing to some pandas DataFrame that is stored as
pickle.
#### Usage
```
compile_germ_eval --help
```
### compile_europeana_historic
Read europeana historic ner ground truth .bio files from directory
and write the outcome of the data parsing to some pandas
DataFrame that is stored as pickle.
#### Usage
```
compile_europeana_historic --help
```
### compile_wikiner
Read wikiner files from directory and write the outcome
of the data parsing to some pandas DataFrame that is stored as pickle.
#### Usage
```
compile_wikiner --help
```
***
## Train BERT - NER model:
### bert-ner
Perform BERT for NER supervised training and test/cross-validation.
#### Usage
```
bert-ner --help
```
## BERT-Pre-training:
### collectcorpus
```
collectcorpus --help
Usage: collectcorpus [OPTIONS] FULLTEXT_FILE SELECTION_FILE CORPUS_FILE
Reads the fulltext from a CSV or SQLITE3 file (see also altotool) and
write it to one big text file.
FULLTEXT_FILE: The CSV or SQLITE3 file to read from.
SELECTION_FILE: Consider only a subset of all pages that is defined by the
DataFrame that is stored in .
CORPUS_FILE: The output file that can be used by bert-pregenerate-trainingdata.
Options:
--chunksize INTEGER Process the corpus in chunks of .
default:10**4
--processes INTEGER Number of parallel processes. default: 6
--min-line-len INTEGER Lower bound of line length in output file.
default:80
--help Show this message and exit.
```
### bert-pregenerate-trainingdata
Generate data for BERT pre-training from a corpus text file where
the documents are separated by an empty line (output of corpuscollect).
#### Usage
```
bert-pregenerate-trainingdata --help
```
### bert-finetune
Perform BERT pre-training on pre-generated data.
#### Usage
```
bert-finetune --help
```