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flair-hipe-2022-topres19th-en

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readme: fix link reference for ByT5 embedding implementation
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metadata
language: en
license: mit
tags:
  - flair
  - token-classification
  - sequence-tagger-model
base_model: hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax
inference: false
widget:
  - text: >-
      On Wednesday , a public dinner was given by the Conservative Burgesses of
      Leads , to the Conservative members of the Leeds Town Council , in the
      Music Hall , Albion-street , which was very numerously attended .

Fine-tuned Flair Model on TopRes19th English NER Dataset (HIPE-2022)

This Flair model was fine-tuned on the TopRes19th English NER Dataset using hmByT5 as backbone LM.

The TopRes19th dataset consists of NE-annotated historical English newspaper articles from 19C.

The following NEs were annotated: BUILDING, LOC and STREET.

⚠️ 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.
bs4-e10-lr0.00015 0.7992 0.8226 0.8205 0.8364 0.809 81.75 ± 1.26
bs8-e10-lr0.00015 0.8095 0.83 0.8024 0.8112 0.8189 81.44 ± 0.94
bs8-e10-lr0.00016 0.8144 0.8209 0.8065 0.8056 0.82 81.35 ± 0.65
bs4-e10-lr0.00016 0.8056 0.8105 0.809 0.808 0.8056 80.77 ± 0.19

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 ❤️