| # NER Fine-Tuning | |
| We use Flair for fine-tuning NER models on | |
| [HIPE-2022](https://github.com/hipe-eval/HIPE-2022-data) datasets from | |
| [HIPE-2022 Shared Task](https://hipe-eval.github.io/HIPE-2022/). | |
| All models are fine-tuned on A10 (24GB) and A100 (40GB) instances from | |
| [Lambda Cloud](https://lambdalabs.com/service/gpu-cloud) using Flair: | |
| ```bash | |
| $ 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: | |
| ```bash | |
| $ 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): | |
| ```bash | |
| # 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`](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)](https://github.com/huggingface/autotrain-advanced) | |
| to fine-tung models. The following snippet shows an example: | |
| ```bash | |
| $ 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`](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] | | |
| [AjMC]: https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-ajmc.md | |
| [NewsEye]: https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-newseye.md | |
| [TopRes19th]: https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-topres19th.md | |
| [ICDAR-Europeana]: https://github.com/stefan-it/historic-domain-adaptation-icdar | |
| [LeTemps]: https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-letemps.md | |
| # 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.](https://ceur-ws.org/Vol-3180/paper-87.pdf) | 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** | | |