Training and evaluating GROBID models


Grobid uses different sequence labelling models depending on the labeling task to be realized. For a complex extraction and parsing tasks (for instance header extraction and parsing), several models are used in cascade. The current models are the following ones:

  • affiliation-address

  • date

  • citation

  • header

  • name-citation

  • name-header

  • patent

  • segmentation

  • reference-segmenter

  • fulltext

  • figure

  • table

  • funding-acknowledgement

The models are located under grobid/grobid-home/models. Each of these models can be retrained using amended or additional training data. For production, a model is trained with all the available training data to maximize the performance. For development purposes, it is also possible to evaluate a model with part of the training data as frozen set (e.g. holdout set), automatic random split or apply 10-fold cross-evaluation.

Train and evaluate

The sub-project grobid-trainer is be used for training. The training data is located under the grobid-trainer/resources folder, more precisely under grobid/grobid-trainer/resources/dataset/*MODEL*/corpus/ where MODEL is the name of the model (so for instance, grobid/grobid-trainer/resources/dataset/date/corpus/).

When generating a new model, a segmentation of data can be done (e.g. 80%-20%) between TEI files for training and for evaluating. This segmentation can be done following two manner:

  • manually: annotated data are moved into two folders, data for training have to be present under grobid/grobid-trainer/resources/dataset/*MODEL*/corpus/, and data for evaluation under grobid/grobid-trainer/resources/dataset/*MODEL*/evaluation/.

  • automatically: The data present under grobid/grobid-trainer/resources/dataset/header/corpus are randomly split following a given ratio (e.g. 0.8 for 80%). The first part is used for training and the second for evaluation.

There are different ways to generate the new model and run the evaluation, whether running the training and the evaluation of the new model separately or not, and whether to split automatically the training data or not. For any methods, the newly generated models are saved directly under grobid-home/models and replace the previous one. A rollback can be made by replacing the newly generated model by the backup record (<model name>.wapiti.old).

Train and evaluation in one command (simple mode)

For simple training without particular parameters, a single command can be used as follow. All the available annotated files under grobid/grobid-trainer/resources/dataset/*MODEL*/corpus will be used for trainng and all available annotated files under grobid/grobid-trainer/resources/dataset/*MODEL*/evaluation/ will be used for evaluation.

Under the main project directory grobid/, run the following command to execute both training and evaluation:

> ./gradlew train_<name_of_model>

Example: train_header, train_date, train_name_header, train_name_citation, train_citation, train_affiliation_address, train_fulltext, train_patent_citation, ...

Examples for training the header model:

> ./gradlew train_header

Examples for training the model for names in header:

> ./gradlew train_name_header 

Train and evaluation separately and using more parameters (full mode)

To have more flexibility and options for training and evaluating the models, use the following commands.

First be sure to have the full project libraries locally built (see Install GROBID for nore details):

> ./gradlew clean install

Under the main project directory grobid/:

Train (generate a new model):

> java -Xmx1024m -Djava.library.path=grobid-home/lib/lin-64:grobid-home/lib/lin-64/jep -jar grobid-trainer/build/libs/grobid-trainer-<current version>-onejar.jar 0 <name of the model> -gH grobid-home

The training files considered are located under grobid/grobid-trainer/resources/dataset/*MODEL*/corpus

The training of the models can be controlled using different parameters. The nbThreads parameter in the configuration file grobid-home/config/grobid.yaml can be increased to speed up the training. Similarly, modifying the stopping criteria can help speed up the training. Please refer this comment to know more.


> java -Xmx1024m -Djava.library.path=grobid-home/lib/lin-64:grobid-home/lib/lin-64/jep -jar grobid-trainer/build/libs/grobid-trainer-<current version>-onejar.jar 1 <name of the model> -gH grobid-home

The considered evaluation files are located under grobid/grobid-trainer/resources/dataset/*MODEL*/evaluation

Automatically split data, train and evaluate:

> java -Xmx1024m -Djava.library.path=grobid-home/lib/lin-64:grobid-home/lib/lin-64/jep -jar grobid-trainer/build/libs/grobid-trainer-<current version>-onejar.jar 2 <name of the model> -gH grobid-home -s <segmentation ratio as a number between 0 and 1, e.g. 0.8 for 80%>

For instance, training the date model with a ratio of 75% for training and 25% for evaluation:

> java -Xmx1024m -Djava.library.path=grobid-home/lib/lin-64:grobid-home/lib/lin-64/jep -jar grobid-trainer/build/libs/grobid-trainer-<current version>-onejar.jar 2 date -gH grobid-home -s 0.75

A ratio of 1.0 means that all the data available under grobid/grobid-trainer/resources/dataset/*MODEL*/corpus/ will be used for training the model, and the evaluation will be empty.

Incremental training:

The previous commands were starting a training from scratch, using all available training data in one training task. Incremental training will start from an existing already train model and apply a further training task using the available training data under grobid/grobid-trainer/resources/dataset/*MODEL*/corpus.

Launching an incremental training is similar as the previous commands, but adding the parameter -i. An existing model under grobid/grobid-home/models/*MODEL* must be available. For example:

> java -Xmx1024m -Djava.library.path=grobid-home/lib/lin-64:grobid-home/lib/lin-64/jep -jar grobid-trainer/build/libs/grobid-trainer-<current version>-onejar.jar 0 <name of the model> -gH grobid-home -i

Note that a full training from scratch with all training data should normally provide better accuracy for a model than several iterative training with a partition of the training data. Using incremental training makes sense for exemple when the model has been trained with a lot of data during days/weeks, and an update is required, or for the development of training data when the update of a model must be quick to generate new trainng data.

In incremental training phases, the training parameters might require some update to stop the training earlier than in normal full training.

N-folds cross-evaluation

For robust evaluation and reporting, n-fold cross-evaluation is commonly used, see the Wikipedia article.

GROBID implementation follows the standard approach, shuffling and dividing the annotated corpus in N equals folds, and performing N training and evaluations, where N-1 folds are used for training and the last one for evaluation. Folds are rotating for each training/evaluation, and thus each fold will be used for evaluation successively at least one time. Finally the evaluation scores for the N folds are averaged, the worst and best training/evaluations being indicated as information.

For performing a N fold evaluation:

> java -Xmx1024m -Djava.library.path=grobid-home/lib/lin-64:grobid-home/lib/lin-64/jep -jar grobid-trainer/build/libs/grobid-trainer-<current version>-onejar.jar 3 <name of the model> -gH grobid-home -n FOLD-NUMBER

FOLD_NUMBER must be > 1.

For instance for a 10-fold evaluation of the date model:

> java -Xmx1024m -Djava.library.path=grobid-home/lib/lin-64:grobid-home/lib/lin-64/jep -jar grobid-trainer/build/libs/grobid-trainer-<current version>-onejar.jar 3 date -gH grobid-home -n 10

Generation of training data

To generate some training datas from some input pdf, the batch grobid-core-<current version>.onejar.jar can be used: Grobid batch (createTraining).

For each pdf in input directory, GROBID generates different files because each model has separate training data, and thus uses separate files. So we have one file for header (*.training.header.tei.xml), one for dates (*, one for names, etc...

When a model uses PDF layout features, an additional feature file (for example *.training.header for the header model) is generated without .tei.xml extension.

If you wish to maintain the training corpus as gold standard, these automatically generated data have to be checked and corrected manually before being moved to the training/evaluation folder of the corresponding model. For correcting/checking these data, the guidelines presented in the next section must be followed to ensure the consistency of the whole training sets.

Training guidelines

Annotation guidelines for creating the training data corresponding to the different GROBID models are available from the following page.