https://github.com/kermitt2/softdata_mentions_client
Python client for software and dataset mention recognizer in scholarly publications, using the Softcite and Datastet services
https://github.com/kermitt2/softdata_mentions_client
dataset pdf python-client scholarly-articles software text
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Python client for software and dataset mention recognizer in scholarly publications, using the Softcite and Datastet services
- Host: GitHub
- URL: https://github.com/kermitt2/softdata_mentions_client
- Owner: kermitt2
- License: apache-2.0
- Created: 2022-06-20T18:56:59.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2022-11-13T13:43:05.000Z (over 3 years ago)
- Last Synced: 2025-07-27T21:45:04.115Z (10 months ago)
- Topics: dataset, pdf, python-client, scholarly-articles, software, text
- Language: Python
- Homepage:
- Size: 64.5 KB
- Stars: 2
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
- License: LICENSE
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README
# Software and Dataset mention recognizer client
Simple Python client for using the Softcite software mention recognition service and the DataStet dataset mention recognition service. It can be applied to:
* individual PDF files
* recursively to a local directory, processing all the encountered PDF
* to a collection of documents harvested by [biblio-glutton-harvester](https://github.com/kermitt2/biblio-glutton-harvester) and [article-dataset-builder](https://github.com/kermitt2/article-dataset-builder), with the benefit of re-using the collection manifest for injectng metadata and keeping track of progress. The collection can be stored locally or on a S3 storage.
The client can call either one of the two services or both, parallelizing queries efficiently for individual or combined services.
## Requirements
The client has been tested with Python 3.6-3.8.
The client requires a working [Softcite software mention recognition service](https://github.com/ourresearch/software-mentions) and/or a working [Datastet dataStet mention recognition service](https://github.com/kermitt2/datastet). Service host and port can be changed in the `config.json` file of the client.
The easiest is to use docker images for running these services:
* Softcite software mention recognition service:
* DataStet dataset mention recognition service:
For acceptable performance, these two services must typically be deployed on two different servers. For good performance, GPU are required to boost the usage of the different involved Deep Learning models.
## Install
```console
> git clone https://github.com/softcite/softdata_mentions_client.git
> cd softdata_mentions_client/
```
It is advised to setup first a virtual environment to avoid falling into one of these gloomy python dependency marshlands:
```console
> virtualenv --system-site-packages -p python3 env
```
```console
> source env/bin/activate
```
Install the dependencies, use:
```console
> pip3 install -r requirements.txt
```
Finally install the project in editable state
```console
> pip3 install -e .
```
## Usage and options
```
usage: client.py [-h] [--repo-in REPO_IN] [--file-in FILE_IN] [--file-out FILE_OUT]
[--data-path DATA_PATH] [--config CONFIG] [--reprocess] [--reset] [--load]
[--diagnostic] [--scorched-earth]
target
Software and Dataset mention recognizer client for Softcite and Datastet services
positional arguments:
target one of [software, dataset, all], mandatory
optional arguments:
-h, --help show this help message and exit
--repo-in REPO_IN path to a directory of PDF files to be processed by the Softcite
software mention recognizer
--file-in FILE_IN a single PDF input file to be processed by the Softcite software
mention recognizer
--file-out FILE_OUT path to a single output the software mentions in JSON format, extracted
from the PDF file-in
--data-path DATA_PATH
path to the resource files created/harvested by biblio-glutton-
harvester
--config CONFIG path to the config file, default is ./config.json
--reprocess reprocessed failed PDF
--reset ignore previous processing states and re-init the annotation process
from the beginning
--load load json files into the MongoDB instance, the --repo-in or --data-path
parameter must indicate the path to the directory of resulting json
files to be loaded, --dump must indicate the path to the json dump file
of document metadata
--diagnostic perform a full count of annotations and diagnostic using MongoDB
regarding the harvesting and transformation process
--scorched-earth remove a PDF file after its sucessful processing in order to save
storage space, careful with this!
```
The logs are written by default in a file `./client.log`, but the location of the logs can be changed in the configuration file (default `./config.json`).
### Processing local PDF files
For processing a single file for both software and dataset mentions, the resulting json being written as file at the indicated output path:
> python3 softdata_mentions_client/client.py all --file-in toto.pdf --file-out toto.json
For processing recursively a directory of PDF files, the results will be:
* written to a mongodb server and database indicated in the config file
* *and* in the directory of PDF files, as json files, together with each processed PDF
> python3 softdata_mentions_client/client.py all --repo-in /mnt/data/biblio/pmc_oa_dir/
The default config file is `./config.json`, but could also be specified via the parameter `--config`:
> python3 softdata_mentions_client/client.py all --repo-in /mnt/data/biblio/pmc_oa_dir/ --config ./my_config.json
To process document for only software mentions:
> python3 softdata_mentions_client/client.py software --file-in toto.pdf --file-out toto.json
and for only dataset mentions:
> python3 softdata_mentions_client/client.py dataset --file-in toto.pdf --file-out toto.json
### Processing a collection of PDF harvested by biblio-glutton-harvester
[biblio-glutton-harvester](https://github.com/kermitt2/biblio-glutton-harvester) and [article-dataset-builder](https://github.com/kermitt2/article-dataset-builder) creates a collection manifest as a LMDB database to keep track of the harvesting of large collection of files. Storage of the resource can be located on a local file system or on a AWS S3 storage. The `software-mention` client will use the collection manifest to process these harvested documents.
* locally:
> python3 softdata_mentions_client/client.py all --data-path /mnt/data/biblio-glutton-harvester/data/
`--data-path` indicates the path to the repository of data harvested by [biblio-glutton-harvester](https://github.com/kermitt2/biblio-glutton-harvester).
The resulting JSON files will be enriched by the metadata records of the processed PDF and will be stored together with each processed PDF in the data repository.
If the harvested collection is located on a S3 storage, the access information must be indicated in the configuration file of the client `config.json`. The extracted software mention will be written in a file with extension `.software.json` and the extracted dataset mentions in a file with extension `.dataset.json` , for example:
```
-rw-rw-r-- 1 lopez lopez 1.1M Aug 8 03:26 0100a44b-6f3f-4cf7-86f9-8ef5e8401567.pdf
-rw-rw-r-- 1 lopez lopez 485 Aug 8 03:41 0100a44b-6f3f-4cf7-86f9-8ef5e8401567.software.json
-rw-rw-r-- 1 lopez lopez 485 Aug 8 03:41 0100a44b-6f3f-4cf7-86f9-8ef5e8401567.dataset.json
```
If a MongoDB server access information is indicated in the configuration file `config.json`, the extracted information will additionally be written in MongoDB.
## License and contact
Distributed under [Apache 2.0 license](http://www.apache.org/licenses/LICENSE-2.0). The dependencies used in the project are either themselves also distributed under Apache 2.0 license or distributed under a compatible license.
If you contribute to this project, you agree to share your contribution following these licenses.
Main author and contact: Patrice Lopez ()