Code of ESWC 2024 submission "Incorporating Type Information Into Zero-Shot Relation Extraction"

Steps to reproduce

1. Unpack the following files:

   • wiki.tar.gz
   • fewrel.tar.gz
   • entity_types_reparsed.pickle.gz
   • types_to_description_alt.json.gz
   • rel_id_title_description_cleaned.jsonl.gz
   • types_to_label.json.gz

2. Run the following command to train the model for each dataset:

   python3 src/train.py ...
   

3. Evaluate on each seed of each dataset by using the following command:

   python3 src/evaluate.py ...
   

Functions

Arguments for train.py:

Argument	Type	Default Value	Description
--dataset_name	str	"fewrel/unseen_5"	Specifies the name of the dataset. This executes the training for all seeds as specified by the --seeds parameter.
--model_type	str	"bert-base-cased"	Specifies the type of model to be used.
--batch_size	int	24	Sets the batch size for training.
--num_workers	int	2	Number of worker processes for data loading.
--accumulate_grad_batches	int	2	Accumulates gradients over a specified number of batches.
--lr	float	5e-5	Learning rate for optimization.
--seeds	int, List	[0, 1, 2, 3, 4]	List of seeds of the dataset to train on.
--include_descriptions	store_true	False	Includes descriptions in the textual representation if this flag is present.
--include_types	store_true	False	Includes types in the textual if this flag is present.

Arguments for evaluate.py:

Argument	Type	Default Value	Required	Description
--model_checkpoint	str	-	Yes	Specifies the path to the model checkpoint.
--dataset_name	str	"fewrel/unseen_5_seed_0"	No	Specifies the name of the dataset with the corresponding seed.
--model_type	str	"bert-base-cased"	No	Specifies the type of model to be used.
--batch_size	int	24	No	Sets the batch size for training.
--num_workers	int	2	No	Number of worker processes for data loading.
--accumulate_grad_batches	int	1	No	Accumulates gradients over a specified number of batches.
--other_properties	int	5	No	Specifies the value for some other properties.
--hard_other_properties	int	0	No	Specifies the value for some other hard properties.
--include_descriptions	store_true	False	No	Includes descriptions in the textual representation if this flag is present.
--include_types	store_true	False	No	Includes types in the textual if this flag is present.
--use_predicted_candidates	store_true	False	No	Uses predicted candidates if this flag is present.