GENRE

Regen is based on GENRE. GENRE is also an entity linking model released by Facebook, but in this case it uses a different approach by conseidering the NERC task as a sequence-to-sequence problem, and retrieves the entities by using a constrained beam search to force the model to generate the entities.

In a nutshell, (m)GENRE uses a sequence-to-sequence approach to entity retrieval (e.g., linking), based on fine-tuned BART. GENRE performs retrieval generating the unique entity name conditioned on the input text using constrained beam search to only generate valid identifiers. Although there is a version end-to-end of GENRE, it is not currently supported on ZShot (but it will).

The REGEN linker will use the entities specified in the zshot.PipelineConfig.

• Paper
• Original Source Code

Bases: Linker

REGEN linker

Parameters:

Name	Type	Description	Default
max_input_len		Max length of input	384
max_output_len		Max length of output	15
num_beams		Number of beans to use	10
trie		If the trie is given the linker will use it to restrict the search space. Custom entities won't be used if the trie is given.	None

load_models()

Load Model

load_tokenizer()

Load Tokenizer

predict(docs, batch_size=None)

Perform the entity prediction

Parameters:

Name	Type	Description	Default
docs	Iterator[Doc]	A list of spacy Document	required
batch_size	Optional[Union[int, None]]	The batch size	None

Returns:

Type	Description
List[List[Span]]	List Spans for each Document in docs

restrict_decode_vocab(_, prefix_beam)

Restrict the possibilities of the Beam search to force the text generation

set_kg(entities)

Set new entities

Parameters:

Name	Type	Description	Default
entities	Iterator[Entity]	New entities to use	required