Registering experimaestro IR library

docs/hub/experimaestro-ir.md

@@ -0,0 +1,68 @@
+# Using experimaestro-IR at Hugging Face
+
+`experimaestro-IR` is an open-source toolkit for neural information retrieval models. It allows using and building experiments around those models, with a focus on reusable components. More up-to-date documentation can be found on the [experimaestro-IR pre-trained model documentation page](https://experimaestro-ir.readthedocs.io/en/latest/pretrained.html).
+
+## Exploring experimaestro-IR in the Hub
+
+You can find `experimaestro-IR` models by filtering at the left of the [models page](https://huggingface.co/models?library=xpmir).
+
+All models on the Hub come up with useful features:
+1. An automatically generated model card with a description and metrics on IR datasets;
+2. Metadata tags that help for discoverability.
+
+
+## Install the library
+To install the `experimaestro-IR` library, you can use pip:
+
+```sh
+pip install experimaestro-ir
+```
+
+## Using existing models
+
+You can simply download a model from the Hub using `xpmir.models.AutoModel`. 
+Thanks to the [experimaestro framework](https://github.com/experimaestro/experimaestro-python), 
+you can either use models in your own experiments or in pure inference mode.
+
+### 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](https://experimaestro-ir.readthedocs.io/)
+for more details.
+
+```py
+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
+
+```py
+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...")
+```
+
+
+## Sharing your models
+
+You can easily upload your models using `AutoModel.push_to_hf_hub`:
+
+```
+from xpmir.models import AutoModel
+
+AutoModel.push_to_hf_hub(model, readme=readme_md)
+```
+
+## Additional resources
+
+* Experimaestro-IR [documentation](https://experimaestro-ir.readthedocs.io/en/latest/pretrained.html)
+* Experimaestro-IR [huggingface integration documentation](https://experimaestro-ir.readthedocs.io/en/latest/pretrained.html)

docs/hub/models-libraries.md

@@ -13,6 +13,7 @@ The table below summarizes the supported libraries and their level of integratio
 | [Asteroid](https://github.com/asteroid-team/asteroid)                       | Pytorch-based audio source separation toolkit                                        | ✅ | ✅ | ✅ | ❌ |
 | [docTR](https://github.com/mindee/doctr)                                    | Models and datasets for OCR-related tasks in PyTorch & TensorFlow                    | ✅ | ✅ | ✅ | ❌ |
 | [ESPnet](https://github.com/espnet/espnet)                                  | End-to-end speech processing toolkit (e.g. TTS)                                      | ✅ | ✅ | ✅ | ❌ |
+| [experimaestro IR](https://github.com/experimaestro/experimaestro-ir)       | Library for (neural) information retrieval                                           | ❌ | ❌ | ✅ | ✅ |
 | [fastai](https://github.com/fastai/fastai)                                  | Library to train fast and accurate models with state-of-the-art outputs.             | ✅ | ✅ | ✅ | ✅ |
 | [Keras](https://huggingface.co/docs/hub/keras)                              | Library that uses a consistent and simple API to build models leveraging TensorFlow and its ecosystem. | ❌ | ❌ | ✅ | ✅ |
 | [Flair](https://github.com/flairNLP/flair)                                  | Very simple framework for state-of-the-art NLP.                                      | ✅ | ✅ | ✅ | ❌ |

js/src/lib/interfaces/Libraries.ts

@@ -33,6 +33,7 @@ export enum ModelLibrary {
 	"stable-baselines3"      = "Stable-Baselines3",
 	"ml-agents"              = "ML-Agents",
 	"pythae"                 = "Pythae",
+	"xpmir"                  = "Experimaestro IR"
 }
 
 export type ModelLibraryKey = keyof typeof ModelLibrary;
@@ -425,6 +426,21 @@ const pythae = (model: ModelData) =>
 
 model = AutoModel.load_from_hf_hub("${model.id}")`;
 
+const xpmir = (model: ModelData) => {
+	if (model.config?.variants?.length > 0) {
+		return `from xpmir.models import AutoModel
+
+// Use a variant among:
+// ${model.config.variants.join(", ")}
+model = AutoModel.load_from_hf_hub("${model.id}", variant)`;
+	}
+
+	return `from xpmir.models import AutoModel
+
+model = AutoModel.load_from_hf_hub("${model.id}")`;
+
+}
+
 //#endregion
 
 
@@ -587,5 +603,11 @@ export const MODEL_LIBRARIES_UI_ELEMENTS: Partial<Record<ModelLibraryKey, Librar
 		repoUrl:  "https://github.com/clementchadebec/benchmark_VAE",
 		snippet:  pythae,
 	},
+	"xpmir": {
+		btnLabel: "xpmir",
+		repoName: "xpmir",
+		repoUrl:  "https://github.com/experimaestro/experimaestro-ir",
+		snippet:  xpmir,
+	}
 } as const;