💼 Fine-Tuned CO-Funer Models
Collection
My fine-tuned Flair models on CO-FUN NER Dataset
•
3 items
•
Updated
Fine-tuned Flair Model on CO-Fun NER Dataset
This Flair model was fine-tuned on the CO-Fun NER Dataset using German 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: [
16,8] - 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 |
0.9346 | 0.9388 | 0.9301 | 0.9291 | 0.9346 | 0.9334 ± 0.0039 |
bs16-e10-lr5e-05 |
0.9316 | 0.9328 | 0.9341 | 0.9315 | 0.9248 | 0.931 ± 0.0036 |
bs8-e10-lr3e-05 |
0.9234 | 0.9391 | 0.9207 | 0.9191 | 0.9394 | 0.9283 ± 0.0101 |
bs16-e10-lr3e-05 |
0.9136 | 0.9269 | 0.9231 | 0.9251 | 0.9247 | 0.9227 ± 0.0053 |
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.
- Downloads last month
- 5
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.
Model tree for stefan-it/flair-co-funer-german_bert_base-bs8-e10-lr5e-05-2
Base model
google-bert/bert-base-german-cased