| --- |
| library_name: span-marker |
| tags: |
| - span-marker |
| - token-classification |
| - ner |
| - named-entity-recognition |
| - generated_from_span_marker_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| widget: [] |
| pipeline_tag: token-classification |
| language: |
| - ar |
| --- |
| |
| # SpanMarker |
|
|
| This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model that can be used for Named Entity Recognition. |
|
|
| ## Model Details |
|
|
| Details are here - https://iahlt.github.io/arabic_ner/ |
| |
| |
| ### Model Description |
| - **Model Type:** SpanMarker |
| <!-- - **Encoder:** [Unknown](https://huggingface.co/unknown) --> |
| - **Maximum Sequence Length:** 512 tokens |
| - **Maximum Entity Length:** 150 words |
| <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
| |
| ### Tags |
| ``` |
| ANG - Any named language (Hebrew, Arabic, English, French, etc.) |
| DUC - A branded product, objects, vehicles, medicines, foods, etc. (Apple, BMW, Coca-Cola, etc.) |
| EVE - Any named event (Olympics, World Cup, etc.) |
| FAC - Any named facility, building, airport, etc. (Eiffel Tower, Ben Gurion Airport, etc.) |
| GPE - Geo-political entity, nation states, counties, cities, etc. |
| INFORMAL - Informal language (slang) |
| LOC - Non-GPE locations, geographical regions, mountain ranges, bodies of water, etc. |
| ORG - Companies, agencies, institutions, political parties, etc. |
| PER - People, including fictional. |
| TIMEX - Time expression, absolute or relative dates or periods. |
| TTL - Any named title, position, profession, etc. (President, Prime Minister, etc.) |
| WOA - Any named work of art (books, movies, songs, etc.) |
| MISC - Miscellaneous entities, that do not belong to the previous categories |
| ``` |
| |
| ## Uses |
| |
| ### Direct Use for Inference |
| |
| ```python |
| from span_marker import SpanMarkerModel |
|
|
| # Download from the 🤗 Hub |
| model = SpanMarkerModel.from_pretrained("iahlt/xlm-roberta-base-ar-ner-flat") |
| entities = model.predict(<text>) |
| print(entities) |
| ``` |
| |
| ## Training Details |
| |
| ### Framework Versions |
| - Python: 3.10.12 |
| - SpanMarker: 1.5.0 |
| - Transformers: 4.35.2 |
| - PyTorch: 2.1.0+cu121 |
| - Datasets: 2.16.1 |
| - Tokenizers: 0.15.1 |
| |
| ## Citation |
| |
| ### BibTeX |
| |
| ``` |
| @software{Aarsen_SpanMarker, |
| author = {Aarsen, Tom}, |
| license = {Apache-2.0}, |
| title = {{SpanMarker for Named Entity Recognition}}, |
| url = {https://github.com/tomaarsen/SpanMarkerNER} |
| } |
| ``` |
| |
