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flair-icdar-nl

Token Classification
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sequence-tagger-model
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metadata
language: nl
license: mit
tags:
  - flair
  - token-classification
  - sequence-tagger-model
base_model: hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax
inference: false
widget:
  - text: >-
      Professoren der Geneeskun dige Faculteit te Groningen alsook van de HH ,
      Doctoren en Chirurgijns van Groningen , Friesland , Noordholland ,
      Overijssel , Gelderland , Drenthe , in welke Provinciën dit Elixir als
      Medicament voor Mond en Tanden reeds jaren bakend is .

Fine-tuned Flair Model on Dutch ICDAR-Europeana NER Dataset

This Flair model was fine-tuned on the Dutch ICDAR-Europeana NER Dataset using hmByT5 as backbone LM.

The ICDAR-Europeana NER Dataset is a preprocessed variant of the Europeana NER Corpora for Dutch and French.

The following NEs were annotated: PER, LOC and ORG.

⚠️ Inference Widget ⚠️

Fine-Tuning ByT5 models in Flair is currently done by implementing an own ByT5Embedding class.

This class needs to be present when running the model with Flair.

Thus, the inference widget is not working with hmByT5 at the moment on the Model Hub and is currently disabled.

This should be fixed in future, when ByT5 fine-tuning is supported in Flair directly.

Results

We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration:

  • Batch Sizes: [8, 4]
  • Learning Rates: [0.00015, 0.00016]

And report micro F1-score on development set:

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
bs8-e10-lr0.00015 0.871 0.8751 0.8748 0.8547 0.8673 86.86 ± 0.75
bs8-e10-lr0.00016 0.8549 0.8713 0.8698 0.8539 0.8665 86.33 ± 0.74
bs4-e10-lr0.00016 0.8516 0.8654 0.87 0.8642 0.8541 86.11 ± 0.7
bs4-e10-lr0.00015 0.8599 0.8649 0.8652 0.8482 0.854 85.84 ± 0.65

The training log and TensorBoard logs (only for hmByT5 and hmTEAMS based models) are also uploaded to the model hub.

More information about fine-tuning can be found here.

Acknowledgements

We thank Luisa März, Katharina Schmid and Erion Çano for their fruitful discussions about Historic Language Models.

Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC). Many Thanks for providing access to the TPUs ❤️