Python client for using the Softcite software mention recognition service at scale. It can be applied to:

• individual PDF or XML fulltext file

• recursively to a local directory, processing all the encountered PDF and XML fulltext files

• to a collection of documents harvested by biblio-glutton-harvester and article-dataset-builder. The collection can be stored locally or on a S3 storage.

The client will handle parallel requests and availability of the server to optimize the processing of a large collection of full texts.

For convenience, the client works also with a DataStet server to run this service at scale similarly as a Softcite software mention recognition service. DataStet extracts dataset mentions from full texts.

The client has been tested with Python 3.5-3.10.

The client requires a working Softcite software mention recognition service or a working Datastet service. The easiest is to use a docker image for these services, see the documentation and latest images at https://hub.docker.com/r/grobid/software-mentions/tags and https://hub.docker.com/r/grobid/datastet/tags.

Service host and port can be changed in the config.json file of the client.

> git clone https://github.com/softcite/software_mentions_client.git
> cd software_mentions_client/

It is advised to setup first a virtual environment to avoid falling into one of these gloomy python dependency marshlands:

> virtualenv --system-site-packages -p python3 env
> source env/bin/activate

Install the dependencies, use:

> python3 -m pip install -r requirements.txt

Finally install the project in editable state

> python3 -m pip install -e .

usage: client.py [-h] [--repo-in REPO_IN] [--file-in FILE_IN] [--file-out FILE_OUT] [--config CONFIG]
                 [--reprocess] [--reset] [--load] [--diagnostic-mongo] [--diagnostic-files]
                 [--scorched-earth] [--datastet]

Softcite software mention recognizer client

optional arguments:
  -h, --help           show this help message and exit
  --repo-in REPO_IN    path to a directory of PDF or XML fulltext files to be processed by the
                       Softcite software mention recognizer
  --file-in FILE_IN    a single PDF or XML 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
  --config CONFIG      path to the config file, default is ./config.json
  --reprocess          reprocessed failed PDF or XML fulltexts
  --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-mongo   perform a full count of annotations and diagnostic using MongoDB regarding the
                       harvesting and annotation process
  --diagnostic-files   perform a full count of annotations and diagnostic using repository files
                       regarding the harvesting and annotation process
  --scorched-earth     remove the PDF or XML fulltext files file after their sucessful processing in
                       order to save storage space, careful with this!
  --datastet           call the DataStet service instead of the software mention extraction service.
                       It requires a DataStet server running instead of the Softcite server, and
                       indicating the Datastet server url in the config file

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).

For processing a single file, the resulting json being written as file at the indicated output path:

python3 -m software_mentions_client.client --file-in toto.pdf --file-out toto.json

The default config file is ./config.json, but could also be specified via the parameter --config:

python3 -m software_mentions_client.client --file-in toto.pdf --file-out toto.json --config ./my_config.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 -m software_mentions_client.client --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 -m software_mentions_client.client --repo-in /mnt/data/biblio/pmc_oa_dir/ --config ./my_config.json

If, for any reason, the process is stopped, it can be resumed by entering the exact same command:

python3 -m software_mentions_client.client --repo-in /mnt/data/biblio/pmc_oa_dir/

This will continue the processing of the input PDF and XML files not yet processed.

Anntations will be added along the PDF and XML files, with extension *.software.json, e.g.:

-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

Just add --reprocess to the command line, the processing will be limited to the PDF and XML files that failed when processing them:

python3 -m software_mentions_client.client --repo-in /mnt/data/biblio/pmc_oa_dir/ --reprocess

For using the DataStet service instead of the Softcite Software Mention service, use the --datastet command line parameter (be sure to indicate the server URL in the configuration file). This alternative mode is provided for convenience and will write results in files with extension *.dataset.json instead of *.software.json. All the other parameters are similar, just the service to be used and the result files will be different.

Using --load option will trigger the loading of all the produced annotations into a MongoDB server indicated in the configuration file.

python3 -m software_mentions_client.client --load 

After the processing of a repository of PDF and XML files, it is possible to get detailed statistics about the produced annotations.

The --diagnostic-files option performs a full count of annotations and diagnostic using repository files regarding the existing file and the annotation process.

python3 -m software_mentions_client.client --diagnostic-file 

The --diagnostic-mongo option performs a full count of annotations and diagnostic using MongoDB regarding the existing files and the annotation process. It supposes that the annotations have been loaded into a MongoDB instance, but it is faster and more complete than the --diagnostic-files option.

python3 -m software_mentions_client.client --diagnostic-mongo 

By default, the concurreny of the parallelized calls to a service is 8. This parameter can be changed in the configuration file config.json.

Other important configuration parameter are the URL of the Software mention recognition web service software_mention_url, the optional URL of a DataStet server if used dataset_mention_url, the MongoDb instance information if you wish to load the produced annotations in MongoDB.

Normally, the configuration parameters sleep_time, timeout and batch_size do not need to be modified to ensure a robust processing.

Distributed under Apache 2.0 license. 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 Softcite software mention recognizer client project, you agree to share your contribution following these licenses.

Main author and contact: Patrice Lopez (patrice.lopez@science-miner.com)