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NER Fine-Tuning

We use Flair for fine-tuning NER models on the AjMC dataset from HIPE-2022 Shared Task.

All models are fine-tuned on A10 (24GB) instances from Lambda Cloud using Flair:

$ git clone -b support_byt5 https://github.com/flairNLP/flair.git && cd flair && pip3 install -e .
$ cd

Clone this repo for fine-tuning NER models:

$ git clone https://github.com/stefan-it/hmByT5.git
$ cd hmByT5/bench

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.

All configurations can be found under the configs/ajmc folder in this repository.

Example command for hyper-parameter search for the hmByT5 model on English part of AjMC corpus:

$ python3 flair-fine-tuner.py ./configs-preliminary/ajmc/en/hmbyt5-small-flax-en.json

To get a nice overview of the results (incl. best hyper-parameter configuration), just run the log parsing script:

$ python3 flair-log-parser.py "hipe2022-flert-fine-tune-ajmc-first-pooling/en-hmbyt5-preliminary/byt5-small-english-bs*"

Small 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 Dataset Additional Dataset
English AjMC -
German AjMC -
French AjMC ICDAR-Europeana
Finnish NewsEye -
Swedish NewsEye -
Dutch ICDAR-Europeana -

Overall Results

The following table shows performance (averaged F1-score on development set, 5 runs) for all models:

Model English AjMC German AjMC French AjMC Finnish NewsEye Swedish NewsEye Dutch ICDAR French ICDAR Avg.
hmbyt5/byt5-small-english 85.65 ± 1.21 87.27 ± 0.50 84.44 ± 0.79
hmbyt5-preliminary/byt5-small-english-german 85.74 ± 0.72 87.45 ± 0.67 84.23 ± 0.65
hmbyt5-preliminary/byt5-small-english-german-french 85.61 ± 0.96 87.24 ± 0.76 84.39 ± 0.68
hmbyt5-preliminary/byt5-small-english-german-french-finnish 85.30 ± 1.14 87.37 ± 0.53 84.12 ± 0.42
hmbyt5-preliminary/byt5-small-english-german-french-finnish-swedish 85.40 ± 0.78 87.12 ± 0.19 84.41 ± 0.34
hmbyt5-preliminary/byt5-small-english-german-french-finnish-swedish-dutch 85.51 ± 0.68 87.58 ± 0.39 84.39 ± 0.83 55.46 ± 1.99 73.38 ± 2.45 84.80 ± 0.44 75.97 ± 0.55
hmbyt5-preliminary/byt5-small-multilingual-4g 83.49 ± 0.96 87.65 ± 0.63 84.16 ± 0.90
hmbyt5-preliminary/byt5-small-multilingual-4g-2e 83.86 ± 0.61 87.54 ± 0.19 84.29 ± 0.41
hmbyt5-preliminary/byt5-small-multilingual-4g-3e 83.49 ± 0.99 87.38 ± 0.53 84.30 ± 0.51
hmbyt5-preliminary/byt5-small-historic-multilingual-flax 83.28 ± 1.67 86.98 ± 0.71 83.49 ± 1.06 76.96 ± 1.58 78.80 ± 1.89 86.47 ± 0.79 77.43 ± 0.51
hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax 84.91 ± 0.86 88.02 ± 0.35 84.78 ± 0.75 77.77 ± 1.83 79.94 ± 0.60 86.85 ± 0.91 77.45 ± 0.54
Detailed results

AjMC English

Model: hmbyt5-preliminary/byt5-small-historic-multilingual

We evaluated the hmByT5 model that was pretrained on English corpus for 200k steps:

Hyper-param Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 83.80 84.78 83.74 83.35 84.37 84.01 ± 0.50
wsFalse-bs4-e10-lr0.00015-poolingfirst 84.67 82.69 83.92 84.53 82.90 83.74 ± 0.82
wsFalse-bs8-e10-lr0.00016-poolingfirst 82.12 83.82 83.37 83.00 83.70 83.20 ± 0.61
wsFalse-bs8-e10-lr0.00015-poolingfirst 83.45 82.83 84.15 81.76 83.78 83.19 ± 0.84

It turns out, that the results are not on-par with current SOTA on the English AjMC corpus, see a comparison here. Thus, we continue experiments with the Hugging Face Transformers JAX/FLAX implementation to pretrain ByT5 models on TPU.

Model: hmbyt5/byt5-small-english

Results with the Hugging Face Transformers JAX/FLAX implementation are really promising. We evaluated a hmByT5 model that was pretrained on English corpus for one epoch:

Hyper-param Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 84.35 84.51 85.21 87.01 87.17 85.65 ± 1.21
wsFalse-bs4-e10-lr0.00015-poolingfirst 85.78 85.03 86.40 85.48 84.47 85.43 ± 0.66
wsFalse-bs8-e10-lr0.00015-poolingfirst 84.70 85.41 85.85 82.94 83.64 84.51 ± 1.08
wsFalse-bs8-e10-lr0.00016-poolingfirst 83.23 85.95 85.41 83.02 84.16 84.35 ± 1.16

Model: hmbyt5-preliminary/byt5-small-english-german

We use the previous hmbyt5-preliminary/byt5-small-english model as initial checkpoint (incl. last learning rate and no warm-up steps) and continue pretraining on the German corpus for one epoch:

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 85.37 85.75 86.7 86.26 84.62 85.74 ± 0.72
wsFalse-bs8-e10-lr0.00016-poolingfirst 84.97 85.31 85.58 84.33 85.27 85.09 ± 0.43
wsFalse-bs4-e10-lr0.00015-poolingfirst 84.64 84.62 85.04 83.92 85.24 84.69 ± 0.45
wsFalse-bs8-e10-lr0.00015-poolingfirst 84.5 84.62 83.41 85.21 83.55 84.26 ± 0.68

Model: hmbyt5-preliminary/byt5-small-english-german-french

We use the previous English+German model as initial checkpoint (incl. last learning rate and no warm-up steps) and continue pretraining on the French corpus for one epoch:

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 86.19 86.8 84.58 86.12 84.36 85.61 ± 0.96
wsFalse-bs4-e10-lr0.00015-poolingfirst 86.12 85.75 83.9 85.17 85.11 85.21 ± 0.75
wsFalse-bs8-e10-lr0.00016-poolingfirst 85.37 85.24 84.9 84.56 84.81 84.98 ± 0.29
wsFalse-bs8-e10-lr0.00015-poolingfirst 85.17 84 84.06 84.49 85.95 84.73 ± 0.74

Model: hmbyt5-preliminary/byt5-small-english-german-french-finnish

We use the previous English+German+French model as initial checkpoint (incl. last learning rate and no warm-up steps) and continue pretraining on the Finnish corpus for one epoch:

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 84.61 87.35 84.06 84.87 85.61 85.30 ± 1.14
wsFalse-bs4-e10-lr0.00015-poolingfirst 86.4 84.51 83.86 84.66 85.48 84.98 ± 0.88
wsFalse-bs8-e10-lr0.00015-poolingfirst 84.73 84.83 84.66 84.5 85.44 84.83 ± 0.32
wsFalse-bs8-e10-lr0.00016-poolingfirst 84.66 85.37 85.2 82.96 84.91 84.62 ± 0.86

Model: hmbyt5-preliminary/byt5-small-english-german-french-finnish-swedish

We use the previous English+German+French+Finnish model as initial checkpoint (incl. last learning rate and no warm-up steps) and continue pretraining on the Swedish corpus for one epoch:

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00015-poolingfirst 84.21 86.02 85.85 86.19 84.75 85.4 ± 0.78
wsFalse-bs8-e10-lr0.00016-poolingfirst 85.95 84.94 84.7 85.41 84.59 85.12 ± 0.5
wsFalse-bs4-e10-lr0.00016-poolingfirst 84.19 85.03 84.25 86.22 85.31 85.0 ± 0.75
wsFalse-bs8-e10-lr0.00015-poolingfirst 84.65 84.26 83.92 85.61 83.96 84.48 ± 0.62

Model: hmbyt5-preliminary/byt5-small-english-german-french-finnish-swedish-dutch

We use the previous English+German+French+Finnish+Swedish model as initial checkpoint (incl. last learning rate and no warm-up steps) and continue pretraining on the Dutch corpus for one epoch:

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 84.35 85.34 85.71 86.46 85.68 85.51 ± 0.68
wsFalse-bs4-e10-lr0.00015-poolingfirst 86.02 86.12 84.16 84.62 86.23 85.43 ± 0.86
wsFalse-bs8-e10-lr0.00016-poolingfirst 84.36 84.93 84.8 84.46 85.55 84.82 ± 0.42
wsFalse-bs8-e10-lr0.00015-poolingfirst 84.67 84.59 84.56 84.63 85 84.69 ± 0.16

Model: hmbyt5-preliminary/byt5-small-multilingual-4g

Results with JAX/FLAX implementation on the multilingual model (4GB of text per language) for one epoch:

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 84.66 84.10 81.79 83.45 83.47 83.49 ± 0.96
wsFalse-bs4-e10-lr0.00015-poolingfirst 83.99 82.85 82.44 84.57 83.49 83.47 ± 0.76
wsFalse-bs8-e10-lr0.00015-poolingfirst 81.96 82.05 82.52 82.13 83.08 82.35 ± 0.41
wsFalse-bs8-e10-lr0.00016-poolingfirst 83.10 81.73 82.46 81.44 82.44 82.23 ± 0.59

Model: hmbyt5-preliminary/byt5-small-multilingual-4g-2e

We use the previous 4GB model as initial checkpoint (incl. last learning rate and no warm-up steps) and continue pretraining on the same corpus for an additional epoch. Pretraining is currently running.

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00015-poolingfirst 84.19 83.62 84.88 83.47 83.16 83.86 ± 0.61
wsFalse-bs4-e10-lr0.00016-poolingfirst 83.57 83.59 82.37 85.58 81.73 83.37 ± 1.32
wsFalse-bs8-e10-lr0.00016-poolingfirst 82.53 82.18 81.88 84.14 82.64 82.67 ± 0.78
wsFalse-bs8-e10-lr0.00015-poolingfirst 82.44 79.4 83.04 81.91 82.49 81.86 ± 1.28

Model: hmbyt5-preliminary/byt5-small-multilingual-4g-3e

We use the previous 4GB model as initial checkpoint (incl. last learning rate and no warm-up steps) and continue pretraining on the same corpus for an additional epoch:

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 84.07 81.92 84.63 84.04 82.77 83.49 ± 0.99
wsFalse-bs4-e10-lr0.00015-poolingfirst 84.27 84.06 84.71 82.11 80.85 83.2 ± 1.47
wsFalse-bs8-e10-lr0.00015-poolingfirst 82.16 81.3 81.73 84.63 81.75 82.31 ± 1.19
wsFalse-bs8-e10-lr0.00016-poolingfirst 80.92 81.54 83.35 82.94 82.09 82.17 ± 0.89

Model: hmbyt5-preliminary/byt5-small-historic-multilingual-flax

Results with JAX/FLAX implementation on the multilingual model for 560k steps (0.5 epochs):

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs8-e10-lr0.00016-poolingfirst 85.04 80.71 82.57 82.92 85.17 83.28 ± 1.67
wsFalse-bs4-e10-lr0.00016-poolingfirst 83.7 82.12 83.12 84.16 83.1 83.24 ± 0.69
wsFalse-bs4-e10-lr0.00015-poolingfirst 83.83 83.76 82.64 84.1 81.49 83.16 ± 0.97
wsFalse-bs8-e10-lr0.00015-poolingfirst 78.65 83.69 80.97 84.02 82.21 81.91 ± 1.96

Model: hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax

Results with JAX/FLAX implementation on the multilingual model for 850k steps (1 epoch):

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 84.63 83.89 85.31 84.37 86.36 84.91 ± 0.86
wsFalse-bs4-e10-lr0.00015-poolingfirst 84.21 85.48 85.14 83.45 83.7 84.4 ± 0.79
wsFalse-bs8-e10-lr0.00016-poolingfirst 83.18 85.82 82.24 82.41 83.35 83.4 ± 1.28
wsFalse-bs8-e10-lr0.00015-poolingfirst 82.51 82.64 83.73 82.53 82.52 82.79 ± 0.47

AjMC German

Model: hmbyt5/byt5-small-english

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00015-poolingfirst 86.91 87.26 86.57 87.98 87.62 87.27 ± 0.5
wsFalse-bs4-e10-lr0.00016-poolingfirst 87.08 86.47 86.02 86.77 87.43 86.75 ± 0.49
wsFalse-bs8-e10-lr0.00016-poolingfirst 85.34 86.12 85.37 86.56 85.99 85.88 ± 0.47
wsFalse-bs8-e10-lr0.00015-poolingfirst 85.58 85.04 85.44 84.36 85.92 85.27 ± 0.53

Model: hmbyt5-preliminary/byt5-small-english-german

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 87.29 88.01 87.17 86.43 88.33 87.45 ± 0.67
wsFalse-bs4-e10-lr0.00015-poolingfirst 88.12 87.58 87.59 86.98 86.94 87.44 ± 0.44
wsFalse-bs8-e10-lr0.00016-poolingfirst 86.78 86.63 85.85 86.64 85.82 86.34 ± 0.42
wsFalse-bs8-e10-lr0.00015-poolingfirst 85.78 85.89 85.58 85.82 85.75 85.76 ± 0.1

Model: hmbyt5-preliminary/byt5-small-english-german-french

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00015-poolingfirst 86.6 86.84 88.06 88.25 86.47 87.24 ± 0.76
wsFalse-bs4-e10-lr0.00016-poolingfirst 86.13 86.54 87.98 86.67 87.53 86.97 ± 0.68
wsFalse-bs8-e10-lr0.00016-poolingfirst 86.98 86.09 87.02 85.99 86.47 86.51 ± 0.43
wsFalse-bs8-e10-lr0.00015-poolingfirst 86.75 86.33 84.53 85.78 85.85 85.85 ± 0.75

Model: hmbyt5-preliminary/byt5-small-english-german-french-finnish

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 87.71 86.98 87.08 86.85 88.25 87.37 ± 0.53
wsFalse-bs4-e10-lr0.00015-poolingfirst 87.19 86.78 87.52 87.25 87.15 87.18 ± 0.24
wsFalse-bs8-e10-lr0.00016-poolingfirst 86.19 86.23 86.47 87.15 86.91 86.59 ± 0.38
wsFalse-bs8-e10-lr0.00015-poolingfirst 85.51 86.29 86.05 85.27 86.8 85.98 ± 0.55

Model: hmbyt5-preliminary/byt5-small-english-german-french-finnish-swedish

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 87.12 86.88 87.43 87.17 86.98 87.12 ± 0.19
wsFalse-bs4-e10-lr0.00015-poolingfirst 88.35 86.33 87.25 86.33 86.78 87.01 ± 0.75
wsFalse-bs8-e10-lr0.00016-poolingfirst 86.74 86.84 86.67 86.98 86.26 86.7 ± 0.24
wsFalse-bs8-e10-lr0.00015-poolingfirst 85.85 85.47 86.54 85.85 84.1 85.56 ± 0.81

Model: hmbyt5-preliminary/byt5-small-english-german-french-finnish-swedish-dutch

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 87.66 88.19 87.56 86.95 87.53 87.58 ± 0.39
wsFalse-bs8-e10-lr0.00016-poolingfirst 87.66 86.84 87.5 86.53 87.23 87.15 ± 0.42
wsFalse-bs4-e10-lr0.00015-poolingfirst 86.33 87.19 87.85 86.9 86.81 87.02 ± 0.5
wsFalse-bs8-e10-lr0.00015-poolingfirst 86.34 84.71 85.75 85.89 85.85 85.71 ± 0.54

Model: hmbyt5-preliminary/byt5-small-multilingual-4g

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00015-poolingfirst 88 88.7 87.45 87.09 86.99 87.65 ± 0.63
wsFalse-bs4-e10-lr0.00016-poolingfirst 87.68 87.74 87.02 87.72 87.14 87.46 ± 0.31
wsFalse-bs8-e10-lr0.00016-poolingfirst 86.54 86.57 86.64 86.5 86.53 86.56 ± 0.05
wsFalse-bs8-e10-lr0.00015-poolingfirst 86.06 86.23 85.44 86.63 86.67 86.21 ± 0.45

Model: hmbyt5-preliminary/byt5-small-multilingual-4g-2e

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 87.72 87.61 87.56 87.66 87.17 87.54 ± 0.19
wsFalse-bs4-e10-lr0.00015-poolingfirst 86.8 86.33 87.29 88.01 86.47 86.98 ± 0.61
wsFalse-bs8-e10-lr0.00016-poolingfirst 85.44 86.95 85.92 85.51 86.27 86.02 ± 0.55
wsFalse-bs8-e10-lr0.00015-poolingfirst 85.24 86.55 85.65 86.67 85.65 85.95 ± 0.56

Model: hmbyt5-preliminary/byt5-small-multilingual-4g-3e

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00015-poolingfirst 87.92 87.48 87.93 86.95 86.6 87.38 ± 0.53
wsFalse-bs4-e10-lr0.00016-poolingfirst 87.15 86.81 87.25 87.74 87.02 87.19 ± 0.31
wsFalse-bs8-e10-lr0.00016-poolingfirst 85.89 86.67 85.58 86.53 85.61 86.06 ± 0.46
wsFalse-bs8-e10-lr0.00015-poolingfirst 86.02 85.61 85.99 86.16 85.75 85.91 ± 0.2

Model: hmbyt5-preliminary/byt5-small-historic-multilingual-flax

Results with JAX/FLAX implementation on the multilingual model for 560k steps (0.5 epochs):

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 87.3 88.16 86.63 86.74 86.06 86.98 ± 0.71
wsFalse-bs4-e10-lr0.00015-poolingfirst 85.92 86.78 85.88 87.22 86.4 86.44 ± 0.51
wsFalse-bs8-e10-lr0.00015-poolingfirst 85.31 85.04 84.83 85.68 84.9 85.15 ± 0.31
wsFalse-bs8-e10-lr0.00016-poolingfirst 85.71 84.86 80.23 85.89 85.68 84.47 ± 2.15

Model: hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 88.48 87.53 87.77 88.33 88 88.02 ± 0.35
wsFalse-bs4-e10-lr0.00015-poolingfirst 87.95 86.71 89.26 87.39 88.78 88.02 ± 0.92
wsFalse-bs8-e10-lr0.00016-poolingfirst 85.61 86.3 86.9 86.6 85.95 86.27 ± 0.46
wsFalse-bs8-e10-lr0.00015-poolingfirst 85.58 85.68 84.96 85.78 85.95 85.59 ± 0.34

AjMC French

Model: hmbyt5/byt5-small-english

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 85.39 84.58 85.11 83.21 83.92 84.44 ± 0.79
wsFalse-bs4-e10-lr0.00015-poolingfirst 84.54 83.71 83.19 83.67 83.52 83.73 ± 0.45
wsFalse-bs8-e10-lr0.00015-poolingfirst 83.15 83.02 83.27 83.58 82.84 83.17 ± 0.25
wsFalse-bs8-e10-lr0.00016-poolingfirst 82.88 82.9 82.63 83.59 83.38 83.08 ± 0.35

Model: hmbyt5-preliminary/byt5-small-english-german

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 84.92 84.22 84.94 83.84 83.23 84.23 ± 0.65
wsFalse-bs4-e10-lr0.00015-poolingfirst 84.01 82.77 83.6 84.04 83.99 83.68 ± 0.48
wsFalse-bs8-e10-lr0.00016-poolingfirst 84.42 82.89 82.9 83.48 82.34 83.21 ± 0.71
wsFalse-bs8-e10-lr0.00015-poolingfirst 82.51 83.65 81.94 83.23 84.25 83.12 ± 0.82

Model: hmbyt5-preliminary/byt5-small-english-german-french

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 84.33 84.82 84.85 83.08 84.86 84.39 ± 0.68
wsFalse-bs4-e10-lr0.00015-poolingfirst 84.62 84.42 84.01 83.67 83.98 84.14 ± 0.34
wsFalse-bs8-e10-lr0.00016-poolingfirst 84.5 83.46 82.85 81.35 83.35 83.1 ± 1.03
wsFalse-bs8-e10-lr0.00015-poolingfirst 83.69 83.23 82.61 82.69 82.99 83.04 ± 0.39

Model: hmbyt5-preliminary/byt5-small-english-german-french-finnish

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00015-poolingfirst 84.2 84.58 84.55 83.56 83.69 84.12 ± 0.42
wsFalse-bs4-e10-lr0.00016-poolingfirst 84.16 84.01 83.81 83.21 83.56 83.75 ± 0.34
wsFalse-bs8-e10-lr0.00016-poolingfirst 81.94 82.52 84.3 84.32 84.03 83.42 ± 1.0
wsFalse-bs8-e10-lr0.00015-poolingfirst 83.1 83.5 83.71 81.74 83.35 83.08 ± 0.7

Model: hmbyt5-preliminary/byt5-small-english-german-french-finnish-swedish

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 84.42 84.92 84.57 84.21 83.91 84.41 ± 0.34
wsFalse-bs4-e10-lr0.00015-poolingfirst 85.07 84.22 83.62 83.91 83.13 83.99 ± 0.65
wsFalse-bs8-e10-lr0.00016-poolingfirst 81.7 84.28 82.94 83.71 83.58 83.24 ± 0.88
wsFalse-bs8-e10-lr0.00015-poolingfirst 82.29 83.56 83.25 83.44 83.07 83.12 ± 0.45

Model: hmbyt5-preliminary/byt5-small-english-german-french-finnish-swedish-dutch

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00015-poolingfirst 85.53 83.83 85.18 83.31 84.11 84.39 ± 0.83
wsFalse-bs4-e10-lr0.00016-poolingfirst 83.31 84.71 84.07 84.03 83.05 83.83 ± 0.59
wsFalse-bs8-e10-lr0.00016-poolingfirst 84.28 84.07 83.25 82.29 82.85 83.35 ± 0.74
wsFalse-bs8-e10-lr0.00015-poolingfirst 82.72 83.95 81.59 84.2 82.16 82.92 ± 1.01

Model: hmbyt5-preliminary/byt5-small-multilingual-4g

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 85.25 83.98 84.63 82.56 84.39 84.16 ± 0.9
wsFalse-bs4-e10-lr0.00015-poolingfirst 83.63 83.56 82.4 82.57 84.44 83.32 ± 0.75
wsFalse-bs8-e10-lr0.00016-poolingfirst 82.37 83.12 81.35 83.33 81.69 82.37 ± 0.77
wsFalse-bs8-e10-lr0.00015-poolingfirst 81.94 82.5 81.47 82.88 81.29 82.02 ± 0.6

Model: hmbyt5-preliminary/byt5-small-multilingual-4g-2e

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 84.63 84.04 84.75 84.42 83.62 84.29 ± 0.41
wsFalse-bs4-e10-lr0.00015-poolingfirst 83.17 82.04 83.6 84.09 83.12 83.2 ± 0.68
wsFalse-bs8-e10-lr0.00016-poolingfirst 82.24 83.29 81.74 82.62 82.34 82.45 ± 0.51
wsFalse-bs8-e10-lr0.00015-poolingfirst 80.75 82.26 82.25 81.75 81.89 81.78 ± 0.55

Model: hmbyt5-preliminary/byt5-small-multilingual-4g-3e

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 84.11 83.98 85.32 83.98 84.11 84.3 ± 0.51
wsFalse-bs4-e10-lr0.00015-poolingfirst 84.47 83.15 84.17 83.73 82.85 83.67 ± 0.61
wsFalse-bs8-e10-lr0.00015-poolingfirst 83.62 81.91 81.48 82.84 82.12 82.39 ± 0.75
wsFalse-bs8-e10-lr0.00016-poolingfirst 83.04 81.24 81.64 82.43 82.19 82.11 ± 0.62

Model: hmbyt5-preliminary/byt5-small-historic-multilingual-flax

Results with JAX/FLAX implementation on the multilingual model for 560k steps (0.5 epochs):

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 84.11 83.08 84.96 83.48 81.8 83.49 ± 1.06
wsFalse-bs4-e10-lr0.00015-poolingfirst 83.31 81.64 82.12 82.91 83.42 82.68 ± 0.69
wsFalse-bs8-e10-lr0.00016-poolingfirst 83.46 81.55 81.84 81.67 82.53 82.21 ± 0.71
wsFalse-bs8-e10-lr0.00015-poolingfirst 81.44 82.53 80.64 83.29 82.27 82.03 ± 0.91

Model: hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00015-poolingfirst 85.07 85.14 83.7 85.82 84.17 84.78 ± 0.75
wsFalse-bs4-e10-lr0.00016-poolingfirst 83.31 82.78 83.94 85.61 83.02 83.73 ± 1.02
wsFalse-bs8-e10-lr0.00016-poolingfirst 82.99 84.89 83.46 82.41 83.92 83.53 ± 0.84
wsFalse-bs8-e10-lr0.00015-poolingfirst 82.4 83.69 83.39 82.84 83.33 83.13 ± 0.46

NewsEye Finnish

Model: hmbyt5-preliminary/byt5-small-historic-multilingual-flax

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 77.73 74.68 79.05 77.71 75.63 76.96 ± 1.58
wsFalse-bs4-e10-lr0.00015-poolingfirst 78.19 76.76 75.85 77.61 74.95 76.67 ± 1.17
wsFalse-bs8-e10-lr0.00016-poolingfirst 74.47 66.95 71.34 53.76 65.81 66.47 ± 7.07
wsFalse-bs8-e10-lr0.00015-poolingfirst 46.39 64.97 66.53 60.81 71.22 61.98 ± 8.48

Model: hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 77.02 77.22 75.54 81.06 78.02 77.77 ± 1.83
wsFalse-bs4-e10-lr0.00015-poolingfirst 78.87 77.87 75.32 78.35 76.69 77.42 ± 1.27
wsFalse-bs8-e10-lr0.00016-poolingfirst 65.82 64.64 78.35 72.93 71.91 70.73 ± 5.01
wsFalse-bs8-e10-lr0.00015-poolingfirst 73.19 75.26 62.34 55.19 62.2 65.64 ± 7.5

Model: hmbyt5-preliminary/byt5-small-english-german-french-finnish-swedish-dutch

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00015-poolingfirst 57.61 55.51 52.95 57.72 53.53 55.46 ± 1.99
wsFalse-bs4-e10-lr0.00016-poolingfirst 55.95 54.99 46.34 53.25 56.73 53.45 ± 3.74
wsFalse-bs8-e10-lr0.00016-poolingfirst 46.06 40.57 46.37 45.74 48.05 45.36 ± 2.52
wsFalse-bs8-e10-lr0.00015-poolingfirst 44.68 42.19 42.15 41.84 44.21 43.01 ± 1.18

NewsEye Swedish

Model: hmbyt5-preliminary/byt5-small-historic-multilingual-flax

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 78.82 78.12 78.18 76.59 82.29 78.8 ± 1.89
wsFalse-bs4-e10-lr0.00015-poolingfirst 79.2 78.91 78.74 77.48 73.7 77.61 ± 2.04
wsFalse-bs8-e10-lr0.00016-poolingfirst 74.46 64.86 71.25 63.91 65.23 67.94 ± 4.16
wsFalse-bs8-e10-lr0.00015-poolingfirst 61.07 62.72 67.03 63.57 68.47 64.57 ± 2.75

Model: hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 80.59 80.29 78.83 80.15 79.85 79.94 ± 0.6
wsFalse-bs4-e10-lr0.00015-poolingfirst 77.31 76.76 79.56 79.85 79.42 78.58 ± 1.28
wsFalse-bs8-e10-lr0.00016-poolingfirst 66.43 65.58 75.09 65.7 64.39 67.44 ± 3.88
wsFalse-bs8-e10-lr0.00015-poolingfirst 65.57 73.51 65.59 64.29 66.67 67.13 ± 3.28

Model: hmbyt5-preliminary/byt5-small-english-german-french-finnish-swedish-dutch

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 73.19 70 75.09 76.95 71.68 73.38 ± 2.45
wsFalse-bs4-e10-lr0.00015-poolingfirst 74.5 69.98 73.68 68.94 75.14 72.45 ± 2.5
wsFalse-bs8-e10-lr0.00016-poolingfirst 61.96 61.34 60.54 59.71 56.58 60.03 ± 1.88
wsFalse-bs8-e10-lr0.00015-poolingfirst 61.26 57.35 61.93 61.2 57.91 59.93 ± 1.9

ICDAR Europeana Dutch

Model: hmbyt5-preliminary/byt5-small-historic-multilingual-flax

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00015-poolingfirst 85.01 87.32 86.41 86.71 86.91 86.47 ± 0.79
wsFalse-bs8-e10-lr0.00016-poolingfirst 85.87 85.79 85.96 85.34 87.29 86.05 ± 0.66
wsFalse-bs8-e10-lr0.00015-poolingfirst 85.41 85.91 87.2 85.14 86.46 86.02 ± 0.74
wsFalse-bs4-e10-lr0.00016-poolingfirst 86.88 86.94 85.77 85.48 84.73 85.96 ± 0.85

Model: hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs8-e10-lr0.00016-poolingfirst 87.11 88.4 86.86 85.86 86.04 86.85 ± 0.91
wsFalse-bs4-e10-lr0.00015-poolingfirst 87.77 86.12 86.76 85.58 87.03 86.65 ± 0.75
wsFalse-bs8-e10-lr0.00015-poolingfirst 86.57 85.16 86.84 86.98 86.05 86.32 ± 0.66
wsFalse-bs4-e10-lr0.00016-poolingfirst 85.99 86.67 86.12 84.72 87.97 86.29 ± 1.05

Model: hmbyt5-preliminary/byt5-small-english-german-french-finnish-swedish-dutch

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs8-e10-lr0.00015-poolingfirst 84.59 84.89 85.58 84.7 84.25 84.8 ± 0.44
wsFalse-bs8-e10-lr0.00016-poolingfirst 84.41 84.63 83.83 84.89 84.75 84.5 ± 0.37
wsFalse-bs4-e10-lr0.00015-poolingfirst 84.14 82.28 85.45 85.87 84.2 84.39 ± 1.25
wsFalse-bs4-e10-lr0.00016-poolingfirst 83.77 82.37 84.31 85.34 83.18 83.79 ± 1.01

ICDAR Europeana French

Model: hmbyt5-preliminary/byt5-small-historic-multilingual-flax

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00016-poolingfirst 77.6 77.77 76.48 77.95 77.34 77.43 ± 0.51
wsFalse-bs4-e10-lr0.00015-poolingfirst 75.74 77.68 77.7 78.59 76.81 77.3 ± 0.96
wsFalse-bs8-e10-lr0.00015-poolingfirst 77.55 77.32 76.77 76.67 76.68 77.0 ± 0.37
wsFalse-bs8-e10-lr0.00016-poolingfirst 77.29 76.78 76.85 77.71 76.27 76.98 ± 0.49

Model: hmbyt5-preliminary/byt5-small-historic-multilingual-span20-flax

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs8-e10-lr0.00016-poolingfirst 77.47 78.04 77.02 78.02 76.68 77.45 ± 0.54
wsFalse-bs8-e10-lr0.00015-poolingfirst 77.31 77.03 76.26 77.69 77.19 77.1 ± 0.47
wsFalse-bs4-e10-lr0.00015-poolingfirst 76.87 76.87 77.18 77.9 76.6 77.08 ± 0.45
wsFalse-bs4-e10-lr0.00016-poolingfirst 76.67 76.64 77.15 77.37 76.97 76.96 ± 0.28

Model: hmbyt5-preliminary/byt5-small-english-german-french-finnish-swedish-dutch

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00015-poolingfirst 75.34 76.69 75.84 75.44 76.52 75.97 ± 0.55
wsFalse-bs4-e10-lr0.00016-poolingfirst 76.38 75.37 74.72 75.54 75.15 75.43 ± 0.55
wsFalse-bs8-e10-lr0.00015-poolingfirst 74.35 76.02 75.92 75.33 74.85 75.29 ± 0.63
wsFalse-bs8-e10-lr0.00016-poolingfirst 74.82 74.93 74.89 75.82 74.02 74.9 ± 0.57

Mean Noise Span Length

The previously pretrained hmByT5 models "accidentally" use a mean noise span length of 3, because this value is the default one for T5. But the ByT5 paper mentions, that using a length of 3 would make pretraining tasks too easy, and recommend a value of 20. We pretrained an English model with mean_noise_span_length=20 and fine-tuned it on English AjMC dataset:

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00015-poolingfirst 85.48 84.6 85.65 86.83 86.53 85.82 ± 0.79
wsFalse-bs4-e10-lr0.00016-poolingfirst 85.35 84.5 86.05 85.1 85.18 85.24 ± 0.5
wsFalse-bs8-e10-lr0.00016-poolingfirst 84.14 83.45 84.4 84.9 85.82 84.54 ± 0.79
wsFalse-bs8-e10-lr0.00015-poolingfirst 85.27 85.3 83.33 85.25 81.7 84.17 ± 1.45

For comparison the model using a length of 3 achieved 85.65 ± 1.21. So we can also see performance improvements when using mean_noise_span_length=20. The model can be found under hmbyt5/byt5-small-historic-english-span20.

We also pretrained a monolingual model for Dutch on the Delpher corpus with both mean_noise_span_length=3 and mean_noise_span_length=20.

Results with mean_noise_span_length=3 on ICDAR Europeana:

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00015-poolingfirst 88.02 88.71 87.17 87 88.62 87.9 ± 0.71
wsFalse-bs8-e10-lr0.00015-poolingfirst 87.1 86.72 87.15 88.29 87.35 87.32 ± 0.53
wsFalse-bs8-e10-lr0.00016-poolingfirst 87.23 87.19 87.11 87.62 87.11 87.25 ± 0.19
wsFalse-bs4-e10-lr0.00016-poolingfirst 85.98 87.5 84.22 87.08 86.48 86.25 ± 1.14

Results with mean_noise_span_length=20 on ICDAR Europeana:

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00015-poolingfirst 86.61 85.88 87.65 87.93 88.01 87.22 ± 0.83
wsFalse-bs8-e10-lr0.00015-poolingfirst 87.88 87.56 85.62 86.52 87.03 86.92 ± 0.8
wsFalse-bs4-e10-lr0.00016-poolingfirst 86.17 85.87 87.77 86.58 87.96 86.87 ± 0.85
wsFalse-bs8-e10-lr0.00016-poolingfirst 87.67 86.02 85.66 87 85.99 86.47 ± 0.75

For the Dutch model on ICDAR Europeana we do not see performance improvements when using mean_noise_span_length=20.

The Dutch monolingual models can be found under:

Base Architecture

In previous experiments, we only train model with ByT5 Small as initial checkpoint. For English and Dutch we trained models with Base Architecture and compare results.

English

On English AjMC the following results can be achieved when using mean_noise_span_length=3:

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs4-e10-lr0.00015-poolingfirst 86.78 87.46 85.75 88.41 86.6 87.0 ± 0.89
wsFalse-bs8-e10-lr0.00016-poolingfirst 86.79 86.29 86.67 87.14 85.82 86.54 ± 0.45
wsFalse-bs4-e10-lr0.00016-poolingfirst 87.04 87.34 86.63 84.09 87.04 86.43 ± 1.19
wsFalse-bs8-e10-lr0.00015-poolingfirst 86.87 86.43 86.88 85.15 85.25 86.12 ± 0.77

With mean_noise_span_length=20:

Configuration Run 1 Run 2 Run 3 Run 4 Run 5 Avg.
wsFalse-bs8-e10-lr0.00015-poolingfirst 86.51 87.2 86.22 85.78 86.46 86.43 ± 0.46
wsFalse-bs4-e10-lr0.00016-poolingfirst 86.12 87.04 87.01 85.25 86.74 86.43 ± 0.68
wsFalse-bs8-e10-lr0.00016-poolingfirst 86.49 85.27 86.12 86.29 85.61 85.96 ± 0.45
wsFalse-bs4-e10-lr0.00015-poolingfirst 86.33 86.05 84.48 85.68 86.16 85.74 ± 0.67

The English monolingual models can be found under: