ReFinED is trained on the entirety of English Wikipedia which consists of over 150M hyperlinks (and title entity mentions). Training the ReFinED model on this large volume of high quality data leads to a good quality general EL model, which can be further fine-tuned on specific datasets.

1. Ensure the ReFinED source directory is in your Python path:

export PYTHONPATH=$PYTHONPATH:src

2. Run the train.py script (to list the arguments and help, run train.py -h):

python3 src/refined/training/train/train.py --experiment_name test

The train.py script will automatically download the training and development split for the Wikipedia hyperlinks dataset and use this for training and evaluation. Training takes around 15 hours per epoch when using 8 V100 32 GB GPUs with a per gpu batch_size of 16.

To use a trained model provide the file path to the directory containing the trained model (requires model.pt and config.json files) to the refined.from_pretrained(...) method as follows:

refined = Refined.from_pretrained(model_name='<absolute_file_path_to_directory_containing_trained_model>',
                                  entity_set='wikipedia',
                                  use_precomputed_descriptions=False)

Then use the refined object as usual.

The steps above will not use precomputed entity description embeddings. This means the embeddings will be computed on-the-fly, which doubles the inference time. To generate precomputed_description_embeddings, run the precompute_description_embeddings.py script (or run refined.precompute_description_embeddings()) and then copy the embeddings file into the directory with the trained model. Lastly, ensure you have use_precomputed_descriptions=True when you call Refined.from_pretrained(...).