stefan-it's picture
readme: fix typo
5784099 verified
metadata
language: de
license: mit
tags:
  - flair
  - token-classification
  - sequence-tagger-model
  - hetzner
  - hetzner-gex44
  - hetzner-gpu
base_model: deepset/gbert-base
datasets:
  - stefan-it/co-funer
widget:
  - text: >-
      Wesentliche Tätigkeiten der Compliance-Funktion wurden an die
      Mercurtainment AG , Düsseldorf , ausgelagert .

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.