Fine-tuned Flair Model on CO-Fun NER Dataset

This Flair model was fine-tuned on the CO-Fun NER Dataset using German DBMDZ BERT as backbone LM.

Dataset

The Company Outsourcing in Fund Prospectuses (CO-Fun) dataset consists of 948 sentences with 5,969 named entity annotations, including 2,340 Outsourced Services, 2,024 Companies, 1,594 Locations and 11 Software annotations.

Overall, the following named entities are annotated:

The latest Flair version is used for fine-tuning.

A hyper-parameter search over the following parameters with 5 different seeds per configuration is performed:

More details can be found in this repository. All models are fine-tuned on a Hetzner GEX44 with an NVIDIA RTX 4000.

A hyper-parameter search with 5 different seeds per configuration is performed and micro F1-score on development set is reported:

Configuration	Seed 1	Seed 2	Seed 3	Seed 4	Seed 5	Average
`bs8-e10-lr5e-05`	0.9378	0.928	0.9383	0.9374	0.9364	0.9356 ± 0.0043
`bs8-e10-lr3e-05`	0.9336	0.9366	0.9299	0.9417	0.9281	0.934 ± 0.0054
`bs16-e10-lr5e-05`	0.927	0.9341	0.9372	0.9283	0.9329	0.9319 ± 0.0042
`bs16-e10-lr3e-05`	0.9141	0.9321	0.9175	0.9391	0.9177	0.9241 ± 0.0109

The result in bold shows the performance of the current viewed model.

Additionally, the Flair training log and TensorBoard logs are also uploaded to the model hub.