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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