YAML Init Parameters

These are the parameters you can specify in pipeline YAML:

Parameter	Type	Possible Values	Description
`model_name_or_path`	String	Default: `shahrukhx01/bert-mini-finetune-question-detection`	Specifies the model you want to use. You can either type a path to the model stored on your computer or the name of a public model from Hugging Face. The default model was trained on the mini BERT architecture and can distinguish between natural language queries and questions. Mandatory.
`model_version`	String	Tag name Branch name Commit hash	The version of the model from Hugging Face. Optional.
`tokenizer`	String	Default: `None`	The name of the tokenizer, usually the same as the model name. Optional.
`use_gpu`	Boolean	`True` (default) `False`	Specifies if GPU should be used. Mandatory.
`task`	String	`text-classification` (default) `zero-shot-classification`	Specifies the type of classification the node should perform. `text-classification` - Choose this task if you have a model trained with a defined list of labels. `zero-shot-classification` - Choose if you want to define labels at runtime. Mandatory.
`labels`	A list of strings	Default: `None`	If you choose `text-classification` as `task` and provide an ordered label, the first label corresponds to output_1, the second label corresponds to output_2, and so on. The labels must match the model labels; only their order can differ. If you selected `zero-shot-classification` as `task`, these are the candidate labels. Mandatory.
`batch_size`	Integer	Default: `16`	The number of queries you want to process at one time. Mandatory.
`progress_bar`	Boolean	`True` (default) `False`	Shows the progress bar when processing queries. Mandatory.
`use_auth_token`	String or Boolean	Default: `None`	Specifies the API token used to download private models from Hugging Face. If you set it to `True`, it uses the token generated when running `transformers-cli login`. Optional.
`devices`	String or torch.device	Default: `None`	A list of torch devices such as cuda, cpu, mps, to limit inference to specific devices. Example: `[torch.device(`cuda:0`), "mps, "cuda:1"` If you set `use_gpu` to `False`, this parameter is not used and a single cpu device is used for inference. Optional.

REST API Runtime Parameters

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