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.