roberta-large-tweetner7-all

This model is a fine-tuned version of roberta-large on the tner/tweetner7 dataset (`train_all` split). Model fine-tuning is done via T-NER's hyper-parameter search (see the repository for more detail). It achieves the following results on the test set of 2021: - F1 (micro): 0.6574551220340903 - Precision (micro): 0.644212629008989 - Recall (micro): 0.6712534690101758 - F1 (macro): 0.6124665667529737 - Precision (macro): 0.6005167968535563 - Recall (macro): 0.625251837701222 The per-entity breakdown of the F1 score on the test set are below: - corporation: 0.5392156862745098 - creative_work: 0.4760582928521859 - event: 0.4673321234119782 - group: 0.6139798488664987 - location: 0.6707399864222675 - person: 0.8293212669683258 - product: 0.6906187624750498 For F1 scores, the confidence interval is obtained by bootstrap as below: - F1 (micro): - 90%: [0.6484148010152769, 0.6672289519134409] - 95%: [0.6470100684797441, 0.6689850350992637] - F1 (macro): - 90%: [0.6484148010152769, 0.6672289519134409] - 95%: [0.6470100684797441, 0.6689850350992637]

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