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kermitt2 / grobid

A machine learning software for extracting information from scholarly documents
https://grobid.readthedocs.io
Apache License 2.0
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bibliographical-references crf deep-learning fulltext hamburger-to-cow machine-learning metadata pdf rnn scientific-articles transformers

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GROBID

License Coverage Status Documentation Status GitHub release Demo grobid.science-miner.com Docker Hub Docker Hub SWH

GROBID documentation

Visit the GROBID documentation for more detailed information.

Summary

GROBID (or Grobid, but not GroBid nor GroBiD) means GeneRation Of BIbliographic Data.

GROBID is a machine learning library for extracting, parsing and re-structuring raw documents such as PDF into structured XML/TEI encoded documents with a particular focus on technical and scientific publications. First developments started in 2008 as a hobby, following a suggestion by Laurent Romary (Inria, France). In 2011, the tool has been made available in open source. Work on GROBID has been steady as a side project since the beginning and is expected to continue as such, facilitated in particular to the continuous support of Inria.

The following functionalities are available:

In a complete PDF processing, GROBID manages 68 final labels used to build relatively fine-grained structures, from traditional publication metadata (title, author first/last/middle names, affiliation types, detailed address, journal, volume, issue, pages, DOI, PMID, etc.) to full text structures (section title, paragraph, reference markers, head/foot notes, figure captions, etc.).

GROBID includes a comprehensive web service API, Docker images, batch processing, a JAVA API, a generic training and evaluation framework (precision, recall, etc., n-fold cross-evaluation), systematic end-to-end benchmarking on thousand documents and the semi-automatic generation of training data.

GROBID can be considered as production ready. Deployments in production includes ResearchGate, Semantic Scholar, HAL Research Archive, scite.ai, Academia.edu, Internet Archive Scholar, INIST-CNRS, CERN (Invenio), and many more. The tool is designed for speed and high scalability in order to address the full scientific literature corpus.

GROBID should run properly "out of the box" on Linux (64 bits) and macOS (Intel and ARM). We cannot ensure currently support for Windows as we did before (help welcome!).

GROBID uses Deep Learning models relying on the DeLFT library, a task-agnostic Deep Learning framework for sequence labelling and text classification, via JEP. GROBID can run Deep Learning architectures (RNN or transformers with or without layout feature channels) or with feature engineered CRF (default), or any mixtures of CRF and DL to balance scalability and accuracy. These models use joint text and visual/layout information provided by pdfalto.

Note that by default the Deep Learning models are not used, only CRF are selected in the default configuration to accommodate "out of the box" hardware. For improved accuracy, you need to select the Deep Learning models to be used in the GROBID configuration file, according to your need and hardware capacities (in particular GPU availability and runtime requirements). Some GROBID Deep Learning models perform significantly better than default CRF, in particular for bibliographical reference parsing, so it is recommended to consider selecting them to use this tool appropriately.

Demo

Demo server

For testing purposes, two public GROBID demo servers are available thanks to HuggingFace, hosted as spaces.

A GROBID demo server with a combination of Deep Learning models and CRF models is available at the following address: https://kermitt2-grobid.hf.space/ or at https://huggingface.co/spaces/kermitt2/grobid. This demo runs however on CPU only. If you have GPU for your own server deployment, it will be significantly faster.

A faster demo with CRF only is available at https://kermitt2-grobid-crf.hf.space/ or https://huggingface.co/spaces/kermitt2/grobid-crf. However, accuracy is lower.

The Web services are documented here.

Warning: Some quota and query limitation apply to the demo server! Please be courteous and do not overload the demo server. For any serious works, you will need to deploy and use your own Grobid server, see the GROBID and Docker containers documentation for doing that easily and activate some Deep Learning models.

Try in Play With Docker

Try in PWD

Wait for 30 seconds for Grobid container to be created before opening a browser tab on port 8080. This demo container runs only with CRF models. Note that there is an additional 60s needed when processing a PDF for the first time for the loading of the models on the "cold" container. Then this Grobid container is available just for you during 4 hours.

Clients

For facilitating the usage GROBID service at scale, we provide clients written in Python, Java, node.js using the web services for parallel batch processing:

A third party client for Go is available offering functionality similar to the Python client:

All these clients will take advantage of the multi-threading for scaling large set of PDF processing. As a consequence, they will be much more efficient than the batch command lines (which use only one thread) and should be preferred.

For example, we have been able to run the complete full-text processing at around 10.6 PDF per second (around 915,000 PDF per day, around 20M pages per day) with the node.js client listed above during one week on one 16 CPU machine (16 threads, 32GB RAM, no SDD, articles from mainstream publishers), see here (11.3M PDF were processed in 6 days by 2 servers without interruption).

In addition, a Java example project is available to illustrate how to use GROBID as a Java library: https://github.com/kermitt2/grobid-example. The example project is using GROBID Java API for extracting header metadata and citations from a PDF and output the results in BibTeX format.

Finally, the following python utilities can be used to create structured full text corpora of scientific articles. The tool simply takes a list of strong identifiers like DOI or PMID, performing the identification of online Open Access PDF, full text harvesting, metadata aggregation and Grobid processing in one workflow at scale: article-dataset-builder

How GROBID works

Visit the documentation page describing the system. To summarize, the key design principles of GROBID are:

Detailed end-to-end benchmarking are available GROBID documentation and continuously updated.

GROBID Modules

A series of additional modules have been developed for performing structure aware text mining directly on scholar PDF, reusing GROBID's PDF processing and sequence labelling weaponry:

Release and changes

See the Changelog.

License

GROBID is distributed under Apache 2.0 license.

The documentation is distributed under CC-0 license and the annotated data under CC-BY license.

If you contribute to GROBID, you agree to share your contribution following these licenses.

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

Sponsors

ej-technologies provided us a free open-source license for its Java Profiler. Click the JProfiler logo below to learn more.

JProfiler

How to cite

If you want to cite this work, please refer to the present GitHub project, together with the Software Heritage project-level permanent identifier. For example, with BibTeX:

@misc{GROBID,
    title = {GROBID},
    howpublished = {\url{https://github.com/kermitt2/grobid}},
    publisher = {GitHub},
    year = {2008--2024},
    archivePrefix = {swh},
    eprint = {1:dir:dab86b296e3c3216e2241968f0d63b68e8209d3c}
}

See the GROBID documentation for more related resources.