Pre-trained models ================== Experimaestro specific pre-trained models can be found on the `HuggingFace Hub `_ searching the `xpmir` library. Models can then be loaded using Using existing models --------------------- You can simply download a model from the Hub using `xpmir.models.AutoModel`. Thanks to the `experimaestro framework `_, you can either use models in your own experiments or in pure inference mode using :py:meth:`load_from_hf_hub() ` As experimental models ---------------------- In this mode, you can reuse the model in your experiments -- e.g. to compare this model with your own, or using it in a complex IR pipeline (e.g. distillation). Please refer to the `experimaestro-IR documentation _` for more details:: from xpmir.models import AutoModel # Model that can be re-used in experiments model = AutoModel.load_from_hf_hub("xpmir/monobert") Pure inference mode ------------------- In this mode, the model can be used right away to score documents:: from xpmir.models import AutoModel # Use this if you want to actually use the model model = AutoModel.load_from_hf_hub("xpmir/monobert", as_instance=True) model.initialize(None) model.rsv("walgreens store sales average", "The average Walgreens salary ranges...") Cross-encoders -------------- Cross-encoders models can also be created from any transformer model that has been trained to classify a query/document using :py:meth:`cross_encoder_model ` Dense models ------------ :py:class:`Dense ` models can also be created from transformers from the Sentence Transformers library (`HuggingFace Hub list `_) using :py:meth:`sentence_scorer `.