NER Fine-Tuning
We use Flair for fine-tuning NER models on HIPE-2022 datasets from HIPE-2022 Shared Task.
All models are fine-tuned on A10 (24GB) and A100 (40GB) instances from Lambda Cloud using Flair:
$ git clone https://github.com/flairNLP/flair.git
$ cd flair && git checkout 419f13a05d6b36b2a42dd73a551dc3ba679f820c
$ pip3 install -e .
$ cd ..
Clone this repo for fine-tuning NER models:
$ git clone https://github.com/stefan-it/hmTEAMS.git
$ cd hmTEAMS/bench
Authorize via Hugging Face CLI (needed because hmTEAMS is currently only available after approval):
# Use access token from https://huggingface.co/settings/tokens
$ huggingface-cli login login
We use a config-driven hyper-parameter search. The script flair-fine-tuner.py can be used to
fine-tune NER models from our Model Zoo.
Benchmark
We test our pretrained language models on various datasets from HIPE-2020, HIPE-2022 and Europeana. The following table shows an overview of used datasets.
| Language | Datasets |
|---|---|
| English | AjMC - TopRes19th |
| German | AjMC - NewsEye |
| French | AjMC - ICDAR-Europeana - LeTemps - NewsEye |
| Finnish | NewsEye |
| Swedish | NewsEye |
| Dutch | ICDAR-Europeana |
Results
We report averaged F1-score over 5 runs with different seeds on development set:
| Model | English AjMC | German AjMC | French AjMC | German NewsEye | French NewsEye | Finnish NewsEye | Swedish NewsEye | Dutch ICDAR | French ICDAR | French LeTemps | English TopRes19th | Avg. |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| hmBERT (32k) Schweter et al. | 85.36 ± 0.94 | 89.08 ± 0.09 | 85.10 ± 0.60 | 39.65 ± 1.01 | 81.47 ± 0.36 | 77.28 ± 0.37 | 82.85 ± 0.83 | 82.11 ± 0.61 | 77.21 ± 0.16 | 65.73 ± 0.56 | 80.94 ± 0.86 | 76.98 |
| hmTEAMS (Ours) | 86.41 ± 0.36 | 88.64 ± 0.42 | 85.41 ± 0.67 | 41.51 ± 2.82 | 83.20 ± 0.79 | 79.27 ± 1.88 | 82.78 ± 0.60 | 88.21 ± 0.39 | 78.03 ± 0.39 | 66.71 ± 0.46 | 81.36 ± 0.59 | 78.32 |