stefan-it/flair-co-funer-gbert_base-bs8-e10-lr3e-05-1

Fine-tuned Flair Model on CO-Fun NER Dataset

This Flair model was fine-tuned on the CO-Fun NER Dataset using GBERT Base 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:

• Auslagerung (engl. outsourcing)
• Unternehmen (engl. company)
• Ort (engl. location)
• Software

Fine-Tuning

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:

• Batch Sizes: [8, 16]
• Learning Rates: [3e-05, 5e-05]

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

Results

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	0.9477	0.935	0.9517	0.9443	0.9342	0.9426 ± 0.0077
bs16-e10-lr5e-05	0.9214	0.9364	0.9334	0.9489	0.9257	0.9332 ± 0.0106
bs8-e10-lr3e-05	0.928	0.9248	0.9421	0.9295	0.9263	0.9301 ± 0.0069
bs16-e10-lr3e-05	0.918	0.9256	0.9331	0.9273	0.9196	0.9247 ± 0.0061

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.