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

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.

Additionally, we provide a script that uses Hugging Face AutoTrain Advanced (Space Runner) to fine-tung models. The following snippet shows an example:

$ pip3 install autotrain-advanced
$ export HF_TOKEN="" # Get token from: https://huggingface.co/settings/tokens
$ autotrain spacerunner --project-name "flair-hipe2022-de-hmteams" \
  --script-path /home/stefan/Repositories/hmTEAMS/bench \
  --username stefan-it \
  --token $HF_TOKEN \
  --backend spaces-t4s \
  --env "CONFIG=configs/hipe2020/de/hmteams.json;HF_TOKEN=$HF_TOKEN;REPO_NAME=stefan-it/autotrain-flair-hipe2022-de-hmteams"

The concrete implementation can be found in script.py.

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