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--- |
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language: |
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- en |
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library_name: flair |
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pipeline_tag: token-classification |
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base_model: FacebookAI/xlm-roberta-large |
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widget: |
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- text: According to the BBC George Washington went to Washington. |
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--- |
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# Flair NER Model trained on CleanCoNLL Dataset |
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This (unofficial) Flair NER model was trained on the awesome [CleanCoNLL](https://aclanthology.org/2023.emnlp-main.533/) dataset. |
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The CleanCoNLL dataset was proposed by Susanna Rücker and Alan Akbik and introduces a corrected version of the classic CoNLL-03 dataset, with updated and more consistent NER labels. |
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## Fine-Tuning |
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We use XLM-RoBERTa Large as backbone language model and the following hyper-parameters for fine-tuning: |
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| Hyper-Parameter | Value | |
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|:--------------- |:-------| |
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| Batch Size | `4` | |
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| Learning Rate | `5-06` | |
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| Max. Epochs | `10` | |
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Additionally, the [FLERT](https://arxiv.org/abs/2011.06993) approach is used for fine-tuning the model. |
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## Results |
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We report micro F1-Score on development (in brackets) and test set for five runs with different seeds: |
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| Seed 1 | Seed 2 | Seed 3 | Seed 4 | Seed 5 | Avg. |
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|:--------------- |:--------------- |:--------------- |:--------------- |:--------------- |:--------------- | |
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| (97.34) / 97.00 | (97.26) / 96.90 | (97.66) / 97.02 | (97.42) / 96.96 | (97.46) / 96.99 | (97.43) / 96.97 | |
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Rücker and Akbik report 96.98 on three different runs, so our results are very close to their reported performance! |
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# Flair Demo |
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The following snippet shows how to use the CleanCoNLL NER models with Flair: |
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```python |
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from flair.data import Sentence |
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from flair.models import SequenceTagger |
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# load tagger |
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tagger = SequenceTagger.load("stefan-it/flair-clean-conll-5") |
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# make example sentence |
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sentence = Sentence("According to the BBC George Washington went to Washington.") |
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# predict NER tags |
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tagger.predict(sentence) |
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# print sentence |
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print(sentence) |
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# print predicted NER spans |
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print('The following NER tags are found:') |
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# iterate over entities and print |
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for entity in sentence.get_spans('ner'): |
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print(entity) |
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``` |
