YAML Init Parameters

These are the parameters you can pass to this component in the pipeline YAML configuration:

Parameter	Type	Possible values	Description
`model`	String	Default: `cross-encoder/ms-marco-MiniLM-L-6-v2`	The name identifier of the model from Hugging Face or the path to a local model folder. Required.
`revision`	String	Default: `None`	The revision of the model to be used. Optional.
`max_seq_len`	Integer	Default: `512`	The maximum number of tokens that a sequence should be truncated to before inference. Required.
`language`	Language	Default: `en`	The language of the data that you want to generate references for. Needed to apply the right sentence splitting rules. Required.
`device`	ComponentDevice	Default: `None`	The device on which the model is loaded. If `None`, the default device is automatically selected. Optional.
`batch_size`	Integer	Default: `16`	The batch size that should be used for inference. Required.
`answer_window_size`	Integer	Default: `1`	The number of sentences of an answer that should be included in one span for inference. Required.
`answer_stride`	Integer	Default: `1`	The stride size for answer window. Required.
`document_window_size`	Integer	Default: `3`	The number of sentences of a document that should be included in one span for inference. Required.
`document_stride`	Integer	Default: `3`	The stride size for document window. Required.
`token`	Secret	Default: `Secret.from_env_var("HF_API_TOKEN", strict=False)`	The token to use as HTTP bearer authorization for remote files. Optional.
`function_to_apply`	String	`sigmoid`, `softmax`, `none` Default: `sigmoid`	The activation function to use on top of the logits. Required.
`min_score_2_label_thresholds`	Dictionary	Default: `None`	The minimum prediction score threshold for each corresponding label. Optional.
`label_2_score_map`	Dictionary	Default: `None`	If using a model with a multi label prediction head, pass in a dictionary mapping label names to a float value. Optional.
`reference_threshold`	Integer	Default: `None`	The minimum score threshold to determine if a prediction should be included as reference or not. Optional.
`default_class`	String	Default: `not_grounded`	A fallback class that should be used if the predicted score doesn't match any threshold. Required.
`verifiability_model`	String	Default: `tstadel/answer-classification-setfit-v2-binary`	The name identifier of the verifiability model to be used on the Hugging Face hub or the path to a local model folder. Optional.
`verifiability_revision`	String	Default: `None`	The revision of the verifiability model to be used. Optional.
`verifiability_batch_size`	Integer	Default: `32`	The batch size that should be used for verifiability inference. Required.
`needs_verification_classes`	List of strings	Default: `["needs_verification"]`	The class names to be used to determine if a sentence needs verification. Required.
`use_split_rules`	Boolean	`True`, `False` Default: `False`	If `True`, additional rules for better splitting answers are applied to the sentence splitting tokenizer. Required.
`extend_abbreviations`	Boolean	`True`, `False` Default: `False`	If `True`, the abbreviations used by NLTK's PunktTokenizer are extended by a list of curated abbreviations if available. If `False`, the default abbreviations are used. Required.
`model_kwargs`	Dictionary	Default: `None`	Additional keyword arguments for the model. Optional.
`verifiability_model_kwargs`	Dictionary	Default: `None`	Additional keyword arguments for the verifiability model. Optional.

REST API Runtime Parameters

There are no runtime parameters you can pass to this component when making a request to the Search REST API endpoint.