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:
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: [
5e-05
,3e-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 |
0.9378 | 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.
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Model tree for stefan-it/flair-co-funer-german_dbmdz_bert_base-bs16-e10-lr5e-05-5
Base model
dbmdz/bert-base-german-cased