Integrate SpanMarker library

docs/hub/_toctree.yml

@@ -77,6 +77,8 @@
         title: Sentence Transformers
       - local: spacy
         title: spaCy
+      - local: span_marker
+        title: SpanMarker
       - local: speechbrain
         title: SpeechBrain
       - local: stable-baselines3

docs/hub/models-libraries.md

@@ -26,6 +26,7 @@ The table below summarizes the supported libraries and their level of integratio
 | [Sample Factory](https://github.com/alex-petrenko/sample-factory)    | Codebase for high throughput asynchronous reinforcement learning.          | ❌ | ✅ | ✅ | ✅ |
 | [Sentence Transformers](https://github.com/UKPLab/sentence-transformers)    | Compute dense vector representations for sentences, paragraphs, and images.          | ✅ | ✅ | ✅ | ✅ |
 | [spaCy](https://github.com/explosion/spaCy)                                 | Advanced Natural Language Processing in Python and Cython.                           | ✅ | ✅ | ✅ | ✅ |
+| [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER)                    | Familiar, simple and state-of-the-art Named Entity Recognition.                      | ✅ | ✅ | ✅ | ✅ |
 | [Scikit Learn (using skops)](https://skops.readthedocs.io/en/stable/)                          | Machine Learning in Python.                           | ✅ | ✅ | ✅ | ✅ |
 | [Speechbrain](https://speechbrain.github.io/)                               | A PyTorch Powered Speech Toolkit.                                                    | ✅ | ✅ | ✅ | ❌ |
 | [Stable-Baselines3](https://github.com/DLR-RM/stable-baselines3)            | Set of reliable implementations of deep reinforcement learning algorithms in PyTorch | ❌ | ✅ | ✅ | ✅ |

docs/hub/span_marker.md

@@ -0,0 +1,63 @@
+# Using SpanMarker at Hugging Face
+
+[SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) is a framework for training powerful Named Entity Recognition models using familiar encoders such as BERT, RoBERTa and DeBERTa. Tightly implemented on top of the 🤗 Transformers library, SpanMarker can take good advantage of it. As a result, SpanMarker will be intuitive to use for anyone familiar with Transformers.
+
+## Exploring SpanMarker in the Hub
+
+You can find `span_marker` models by filtering at the left of the [models page](https://huggingface.co/models?library=span_marker).
+
+All models on the Hub come with these useful features:
+1. An automatically generated model card with a brief description.
+2. An interactive widget you can use to play with the model directly in the browser.
+3. An Inference API that allows you to make inference requests.
+
+## Installation
+
+To get started, you can follow the [SpanMarker installation guide](https://tomaarsen.github.io/SpanMarkerNER/install.html). You can also use the following one-line install through pip:
+
+```
+pip install -U span_marker
+```
+
+## Using existing models
+
+All `span_marker` models can easily be loaded from the Hub.
+
+```py
+from span_marker import SpanMarkerModel
+
+model = SpanMarkerModel.from_pretrained("tomaarsen/span-marker-bert-base-fewnerd-fine-super")
+```
+
+Once loaded, you can use [`SpanMarkerModel.predict`](https://tomaarsen.github.io/SpanMarkerNER/api/span_marker.modeling.html#span_marker.modeling.SpanMarkerModel.predict) to perform inference.
+
+```py
+model.predict("Amelia Earhart flew her single engine Lockheed Vega 5B across the Atlantic to Paris.")
+```
+```json
+[
+    {"span": "Amelia Earhart", "label": "person-other", "score": 0.7629689574241638, "char_start_index": 0, "char_end_index": 14},
+    {"span": "Lockheed Vega 5B", "label": "product-airplane", "score": 0.9833564758300781, "char_start_index": 38, "char_end_index": 54},
+    {"span": "Atlantic", "label": "location-bodiesofwater", "score": 0.7621214389801025, "char_start_index": 66, "char_end_index": 74},
+    {"span": "Paris", "label": "location-GPE", "score": 0.9807717204093933, "char_start_index": 78, "char_end_index": 83}
+]
+```
+
+If you want to load a specific SpanMarker model, you can click `Use in SpanMarker` and you will be given a working snippet!
+
+<!--
+TODO: Add this, but then with SpanMarker
+<div class="flex justify-center">
+<img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/libraries-speechbrain_snippet1.png"/>
+<img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/libraries-speechbrain_snippet1-dark.png"/>
+</div>
+<div class="flex justify-center">
+<img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/libraries-speechbrain_snippet2.png"/>
+<img class="hidden dark:block" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/libraries-speechbrain_snippet2-dark.png"/>
+</div>
+-->
+
+## Additional resources
+
+* SpanMarker [repository](https://github.com/tomaarsen/SpanMarkerNER)
+* SpanMarker [docs](https://tomaarsen.github.io/SpanMarkerNER)

js/src/lib/interfaces/Libraries.ts

@@ -23,6 +23,7 @@ export enum ModelLibrary {
 	"sentence-transformers"  = "Sentence Transformers",
 	"sklearn"                = "Scikit-learn",
 	"spacy"                  = "spaCy",
+	"span-marker"            = "SpanMarker",
 	"speechbrain"            = "speechbrain",
 	"tensorflowtts"          = "TensorFlowTTS",
 	"timm"                   = "Timm",
@@ -314,6 +315,11 @@ nlp = spacy.load("${nameWithoutNamespace(model.id)}")
 import ${nameWithoutNamespace(model.id)}
 nlp = ${nameWithoutNamespace(model.id)}.load()`;
 
+const span_marker = (model: ModelData) =>
+	`from span_marker import SpanMarkerModel
+
+	model = SpanMarkerModel.from_pretrained("${model.id}")`;
+
 const stanza = (model: ModelData) =>
 	`import stanza
 
@@ -528,6 +534,12 @@ export const MODEL_LIBRARIES_UI_ELEMENTS: Partial<Record<ModelLibraryKey, Librar
 		repoUrl:  "https://github.com/explosion/spaCy",
 		snippet:  spacy,
 	},
+	"span-marker": {
+		btnLabel: "SpanMarker",
+		repoName: "SpanMarkerNER",
+		repoUrl:  "https://github.com/tomaarsen/SpanMarkerNER",
+		snippet:  span_marker,
+	},
 	"speechbrain": {
 		btnLabel: "speechbrain",
 		repoName: "speechbrain",

js/src/lib/interfaces/LibrariesToTasks.ts

@@ -82,6 +82,9 @@ export const LIBRARY_TASK_MAPPING_EXCLUDING_TRANSFORMERS: Partial<Record<ModelLi
 		"text-classification",
 		"sentence-similarity",
 	],
+	"span-marker": [
+		"token-classification",
+	],
 	"speechbrain": [
 		"audio-classification",
 		"audio-to-audio",

tasks/src/const.ts

@@ -40,7 +40,7 @@ export const TASKS_MODEL_LIBRARIES: Record<PipelineType, ModelLibraryKey[]> = {
 	"text-to-video":                  [],
 	"text2text-generation":           ["transformers"],
 	"time-series-forecasting":        [],
-	"token-classification":           ["adapter-transformers", "flair", "spacy", "stanza", "transformers"],
+	"token-classification":           ["adapter-transformers", "flair", "spacy", "span-marker", "stanza", "transformers"],
 	"translation":                    ["transformers"],
 	"unconditional-image-generation": [],
 	"visual-question-answering":      [],