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--- |
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language: |
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- ace |
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- af |
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- als |
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- am |
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- an |
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- ang |
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- ar |
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- arc |
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- arz |
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- as |
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- ast |
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- ay |
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- az |
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- ba |
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- bar |
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- be |
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- bg |
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- bh |
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- bn |
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- bo |
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- br |
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- bs |
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- ca |
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- cbk |
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- cdo |
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- ce |
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- ceb |
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- ckb |
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- co |
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- crh |
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- cs |
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- csb |
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- cv |
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- cy |
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- da |
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- de |
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- diq |
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- dv |
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- el |
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- eml |
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- en |
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- eo |
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- es |
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- et |
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- eu |
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- ext |
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- fa |
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- fi |
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- fo |
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- fr |
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- frr |
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- fur |
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- fy |
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- ga |
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- gan |
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- gd |
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- gl |
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- gn |
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- gu |
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- hak |
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- he |
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- hi |
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- hr |
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- hsb |
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- hu |
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- hy |
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- ia |
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- id |
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- ig |
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- ilo |
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- io |
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- is |
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- it |
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- ja |
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- jbo |
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- jv |
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- ka |
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- kk |
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- km |
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- kn |
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- ko |
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- ksh |
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- ku |
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- ky |
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- la |
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- lb |
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- li |
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- lij |
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- lmo |
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- ln |
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- lt |
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- lv |
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- lzh |
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- mg |
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- mhr |
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- mi |
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- min |
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- mk |
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- ml |
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- mn |
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- mr |
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- ms |
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- mt |
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- mwl |
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- my |
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- mzn |
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- nan |
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- nap |
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- nds |
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- ne |
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- nl |
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- nn |
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- 'no' |
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- nov |
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- oc |
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- or |
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- os |
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- pa |
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- pdc |
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- pl |
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- pms |
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- pnb |
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- ps |
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- pt |
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- qu |
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- rm |
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- ro |
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- ru |
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- rw |
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- sa |
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- sah |
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- scn |
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- sco |
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- sd |
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- sgs |
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- sh |
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- si |
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- sk |
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- sl |
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- so |
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- sq |
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- sr |
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- su |
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- sv |
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- sw |
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- szl |
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- ta |
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- te |
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- tg |
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- th |
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- tk |
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- tl |
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- tr |
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- tt |
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- ug |
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- uk |
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- ur |
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- uz |
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- vec |
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- vep |
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- vi |
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- vls |
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- vo |
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- vro |
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- wa |
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- war |
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- wuu |
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- xmf |
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- yi |
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- yo |
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- yue |
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- zea |
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- zh |
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license: other |
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library_name: span-marker |
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tags: |
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- span-marker |
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- token-classification |
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- ner |
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- named-entity-recognition |
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- generated_from_span_marker_trainer |
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datasets: |
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- wikiann |
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metrics: |
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- precision |
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- recall |
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- f1 |
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widget: |
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- text: جامعة بيزا (إيطاليا). |
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- text: تعلم في جامعة أوكسفورد، جامعة برنستون، جامعة كولومبيا. |
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- text: موطنها بلاد الشام تركيا. |
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- text: عادل إمام - نور الشريف |
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- text: فوكسي و بورتشا ضد مونكي دي لوفي و نامي |
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pipeline_tag: token-classification |
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base_model: xlm-roberta-base |
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model-index: |
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- name: SpanMarker with xlm-roberta-base on wikiann |
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results: |
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- task: |
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type: token-classification |
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name: Named Entity Recognition |
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dataset: |
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name: Unknown |
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type: wikiann |
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split: eval |
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metrics: |
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- type: f1 |
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value: 0.8965362325351544 |
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name: F1 |
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- type: precision |
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value: 0.9077510917030568 |
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name: Precision |
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- type: recall |
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value: 0.8855951007366646 |
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name: Recall |
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--- |
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|
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# SpanMarker with xlm-roberta-base on wikiann |
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|
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This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [wikiann](https://huggingface.co/datasets/wikiann) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) as the underlying encoder. |
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|
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## Model Details |
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### Model Description |
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- **Model Type:** SpanMarker |
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- **Encoder:** [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) |
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- **Maximum Sequence Length:** 512 tokens |
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- **Maximum Entity Length:** 30 words |
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- **Training Dataset:** [wikiann](https://huggingface.co/datasets/wikiann) |
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- **Languages:** ace, af, als, am, an, ang, ar, arc, arz, as, ast, ay, az, ba, bar, be, bg, bh, bn, bo, br, bs, ca, cbk, cdo, ce, ceb, ckb, co, crh, cs, csb, cv, cy, da, de, diq, dv, el, eml, en, eo, es, et, eu, ext, fa, fi, fo, fr, frr, fur, fy, ga, gan, gd, gl, gn, gu, hak, he, hi, hr, hsb, hu, hy, ia, id, ig, ilo, io, is, it, ja, jbo, jv, ka, kk, km, kn, ko, ksh, ku, ky, la, lb, li, lij, lmo, ln, lt, lv, lzh, mg, mhr, mi, min, mk, ml, mn, mr, ms, mt, mwl, my, mzn, nan, nap, nds, ne, nl, nn, no, nov, oc, or, os, pa, pdc, pl, pms, pnb, ps, pt, qu, rm, ro, ru, rw, sa, sah, scn, sco, sd, sgs, sh, si, sk, sl, so, sq, sr, su, sv, sw, szl, ta, te, tg, th, tk, tl, tr, tt, ug, uk, ur, uz, vec, vep, vi, vls, vo, vro, wa, war, wuu, xmf, yi, yo, yue, zea, zh |
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- **License:** other |
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### Model Sources |
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- **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER) |
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- **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf) |
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### Model Labels |
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| Label | Examples | |
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|:------|:-----------------------------------------------------------------------| |
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| LOC | "شور بلاغ ( مقاطعة غرمي )", "دهنو ( تایباد )", "أقاليم ما وراء البحار" | |
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| ORG | "الحزب الاشتراكي", "نادي باسوش دي فيريرا", "دايو ( شركة )" | |
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| PER | "فرنسوا ميتيران،", "ديفيد نالبانديان", "حكم ( كرة قدم )" | |
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## Uses |
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### Direct Use for Inference |
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```python |
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from span_marker import SpanMarkerModel |
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# Download from the 🤗 Hub |
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model = SpanMarkerModel.from_pretrained("span_marker_model_id") |
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# Run inference |
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entities = model.predict("موطنها بلاد الشام تركيا.") |
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``` |
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### Downstream Use |
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You can finetune this model on your own dataset. |
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|
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<details><summary>Click to expand</summary> |
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```python |
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from span_marker import SpanMarkerModel, Trainer |
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# Download from the 🤗 Hub |
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model = SpanMarkerModel.from_pretrained("span_marker_model_id") |
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# Specify a Dataset with "tokens" and "ner_tag" columns |
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dataset = load_dataset("conll2003") # For example CoNLL2003 |
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# Initialize a Trainer using the pretrained model & dataset |
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trainer = Trainer( |
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model=model, |
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train_dataset=dataset["train"], |
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eval_dataset=dataset["validation"], |
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) |
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trainer.train() |
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trainer.save_model("span_marker_model_id-finetuned") |
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``` |
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</details> |
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## Training Details |
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### Training Set Metrics |
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| Training set | Min | Median | Max | |
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|:----------------------|:----|:-------|:----| |
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| Sentence length | 3 | 6.4592 | 63 | |
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| Entities per sentence | 1 | 1.1251 | 13 | |
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### Training Hyperparameters |
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- learning_rate: 1e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 10 |
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### Training Results |
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| Epoch | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy | |
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|:------:|:-----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:| |
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| 0.1989 | 500 | 0.1735 | 0.2667 | 0.0011 | 0.0021 | 0.4103 | |
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| 0.3979 | 1000 | 0.0808 | 0.7283 | 0.5314 | 0.6145 | 0.7716 | |
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| 0.5968 | 1500 | 0.0595 | 0.7876 | 0.6872 | 0.7340 | 0.8546 | |
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| 0.7957 | 2000 | 0.0532 | 0.8148 | 0.7600 | 0.7865 | 0.8823 | |
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| 0.9946 | 2500 | 0.0478 | 0.8485 | 0.8028 | 0.8250 | 0.9085 | |
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| 1.1936 | 3000 | 0.0419 | 0.8586 | 0.8084 | 0.8327 | 0.9101 | |
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| 1.3925 | 3500 | 0.0390 | 0.8628 | 0.8367 | 0.8495 | 0.9237 | |
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| 1.5914 | 4000 | 0.0456 | 0.8559 | 0.8299 | 0.8427 | 0.9231 | |
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| 1.7903 | 4500 | 0.0375 | 0.8682 | 0.8469 | 0.8574 | 0.9282 | |
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| 1.9893 | 5000 | 0.0323 | 0.8821 | 0.8635 | 0.8727 | 0.9348 | |
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| 2.1882 | 5500 | 0.0346 | 0.8781 | 0.8632 | 0.8706 | 0.9346 | |
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| 2.3871 | 6000 | 0.0318 | 0.8953 | 0.8523 | 0.8733 | 0.9345 | |
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| 2.5860 | 6500 | 0.0311 | 0.8861 | 0.8691 | 0.8775 | 0.9373 | |
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| 2.7850 | 7000 | 0.0323 | 0.89 | 0.8689 | 0.8793 | 0.9383 | |
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| 2.9839 | 7500 | 0.0310 | 0.8892 | 0.8780 | 0.8836 | 0.9419 | |
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| 3.1828 | 8000 | 0.0320 | 0.8817 | 0.8762 | 0.8790 | 0.9397 | |
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| 3.3817 | 8500 | 0.0291 | 0.8981 | 0.8778 | 0.8878 | 0.9438 | |
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| 3.5807 | 9000 | 0.0336 | 0.8972 | 0.8792 | 0.8881 | 0.9450 | |
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| 3.7796 | 9500 | 0.0323 | 0.8927 | 0.8757 | 0.8841 | 0.9424 | |
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| 3.9785 | 10000 | 0.0315 | 0.9028 | 0.8748 | 0.8886 | 0.9436 | |
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| 4.1774 | 10500 | 0.0330 | 0.8984 | 0.8855 | 0.8919 | 0.9458 | |
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| 4.3764 | 11000 | 0.0315 | 0.9023 | 0.8844 | 0.8933 | 0.9469 | |
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| 4.5753 | 11500 | 0.0305 | 0.9029 | 0.8886 | 0.8957 | 0.9486 | |
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| 4.6171 | 11605 | 0.0323 | 0.9078 | 0.8856 | 0.8965 | 0.9487 | |
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### Framework Versions |
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- Python: 3.10.12 |
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- SpanMarker: 1.4.0 |
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- Transformers: 4.34.1 |
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- PyTorch: 2.1.0+cu118 |
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- Datasets: 2.14.6 |
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- Tokenizers: 0.14.1 |
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## Citation |
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### BibTeX |
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``` |
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@software{Aarsen_SpanMarker, |
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author = {Aarsen, Tom}, |
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license = {Apache-2.0}, |
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title = {{SpanMarker for Named Entity Recognition}}, |
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url = {https://github.com/tomaarsen/SpanMarkerNER} |
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} |
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``` |
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