dbmdz/bert-tiny-historic-multilingual-cased

Historic Language Models (HLMs)

Languages

Our Historic Language Models Zoo contains support for the following languages - incl. their training data source:

Language	Training data	Size
German	Europeana	13-28GB (filtered)
French	Europeana	11-31GB (filtered)
English	British Library	24GB (year filtered)
Finnish	Europeana	1.2GB
Swedish	Europeana	1.1GB

Models

At the moment, the following models are available on the model hub:

Model identifier	Model Hub link
dbmdz/bert-base-historic-multilingual-cased	here
dbmdz/bert-base-historic-english-cased	here
dbmdz/bert-base-finnish-europeana-cased	here
dbmdz/bert-base-swedish-europeana-cased	here

We also released smaller models for the multilingual model:

Model identifier	Model Hub link
dbmdz/bert-tiny-historic-multilingual-cased	here
dbmdz/bert-mini-historic-multilingual-cased	here
dbmdz/bert-small-historic-multilingual-cased	here
dbmdz/bert-medium-historic-multilingual-cased	here

Notice: We have released language models for Historic German and French trained on more noisier data earlier - see this repo for more information:

Model identifier	Model Hub link
dbmdz/bert-base-german-europeana-cased	here
dbmdz/bert-base-french-europeana-cased	here

Corpora Stats

German Europeana Corpus

We provide some statistics using different thresholds of ocr confidences, in order to shrink down the corpus size and use less-noisier data:

OCR confidence	Size
0.60	28GB
0.65	18GB
0.70	13GB

For the final corpus we use a OCR confidence of 0.6 (28GB). The following plot shows a tokens per year distribution:

French Europeana Corpus

Like German, we use different ocr confidence thresholds:

OCR confidence	Size
0.60	31GB
0.65	27GB
0.70	27GB
0.75	23GB
0.80	11GB

For the final corpus we use a OCR confidence of 0.7 (27GB). The following plot shows a tokens per year distribution:

British Library Corpus

Metadata is taken from here. Stats incl. year filtering:

Years	Size
ALL	24GB
>= 1800 && < 1900	24GB

We use the year filtered variant. The following plot shows a tokens per year distribution:

Finnish Europeana Corpus

OCR confidence	Size
0.60	1.2GB

The following plot shows a tokens per year distribution:

Swedish Europeana Corpus

OCR confidence	Size
0.60	1.1GB

The following plot shows a tokens per year distribution:

All Corpora

The following plot shows a tokens per year distribution of the complete training corpus:

Multilingual Vocab generation

For the first attempt, we use the first 10GB of each pretraining corpus. We upsample both Finnish and Swedish to ~10GB. The following tables shows the exact size that is used for generating a 32k and 64k subword vocabs:

Language	Size
German	10GB
French	10GB
English	10GB
Finnish	9.5GB
Swedish	9.7GB

We then calculate the subword fertility rate and portion of [UNK]s over the following NER corpora:

Language	NER corpora
German	CLEF-HIPE, NewsEye
French	CLEF-HIPE, NewsEye
English	CLEF-HIPE
Finnish	NewsEye
Swedish	NewsEye

Breakdown of subword fertility rate and unknown portion per language for the 32k vocab:

Language	Subword fertility	Unknown portion
German	1.43	0.0004
French	1.25	0.0001
English	1.25	0.0
Finnish	1.69	0.0007
Swedish	1.43	0.0

Breakdown of subword fertility rate and unknown portion per language for the 64k vocab:

Language	Subword fertility	Unknown portion
German	1.31	0.0004
French	1.16	0.0001
English	1.17	0.0
Finnish	1.54	0.0007
Swedish	1.32	0.0

Final pretraining corpora

We upsample Swedish and Finnish to ~27GB. The final stats for all pretraining corpora can be seen here:

Language	Size
German	28GB
French	27GB
English	24GB
Finnish	27GB
Swedish	27GB

Total size is 130GB.

Smaller multilingual models

Inspired by the "Well-Read Students Learn Better: On the Importance of Pre-training Compact Models" paper, we train smaller models (different layers and hidden sizes), and report number of parameters and pre-training costs:

Model (Layer / Hidden size)	Parameters	Pre-Training time
hmBERT Tiny ( 2/128)	4.58M	4.3 sec / 1,000 steps
hmBERT Mini ( 4/256)	11.55M	10.5 sec / 1,000 steps
hmBERT Small ( 4/512)	29.52M	20.7 sec / 1,000 steps
hmBERT Medium ( 8/512)	42.13M	35.0 sec / 1,000 steps
hmBERT Base (12/768)	110.62M	80.0 sec / 1,000 steps

We then perform downstream evaluations on the multilingual NewsEye dataset:

Pretraining

Multilingual model - hmBERT Base

We train a multilingual BERT model using the 32k vocab with the official BERT implementation on a v3-32 TPU using the following parameters:

python3 run_pretraining.py --input_file gs://histolectra/historic-multilingual-tfrecords/*.tfrecord \
--output_dir gs://histolectra/bert-base-historic-multilingual-cased \
--bert_config_file ./config.json \
--max_seq_length=512 \
--max_predictions_per_seq=75 \
--do_train=True \
--train_batch_size=128 \
--num_train_steps=3000000 \
--learning_rate=1e-4 \
--save_checkpoints_steps=100000 \
--keep_checkpoint_max=20 \
--use_tpu=True \
--tpu_name=electra-2 \
--num_tpu_cores=32

The following plot shows the pretraining loss curve:

Smaller multilingual models

We use the same parameters as used for training the base model.

hmBERT Tiny

The following plot shows the pretraining loss curve for the tiny model:

hmBERT Mini

The following plot shows the pretraining loss curve for the mini model:

hmBERT Small

The following plot shows the pretraining loss curve for the small model:

hmBERT Medium

The following plot shows the pretraining loss curve for the medium model:

English model

The English BERT model - with texts from British Library corpus - was trained with the Hugging Face JAX/FLAX implementation for 10 epochs (approx. 1M steps) on a v3-8 TPU, using the following command:

python3 run_mlm_flax.py --model_type bert \
--config_name /mnt/datasets/bert-base-historic-english-cased/ \
--tokenizer_name /mnt/datasets/bert-base-historic-english-cased/ \
--train_file /mnt/datasets/bl-corpus/bl_1800-1900_extracted.txt \
--validation_file /mnt/datasets/bl-corpus/english_validation.txt \
--max_seq_length 512 \
--per_device_train_batch_size 16 \
--learning_rate 1e-4 \
--num_train_epochs 10 \
--preprocessing_num_workers 96 \
--output_dir /mnt/datasets/bert-base-historic-english-cased-512-noadafactor-10e \
--save_steps 2500 \
--eval_steps 2500 \
--warmup_steps 10000 \
--line_by_line \
--pad_to_max_length

The following plot shows the pretraining loss curve:

Finnish model

The BERT model - with texts from Finnish part of Europeana - was trained with the Hugging Face JAX/FLAX implementation for 40 epochs (approx. 1M steps) on a v3-8 TPU, using the following command:

python3 run_mlm_flax.py --model_type bert \
--config_name /mnt/datasets/bert-base-finnish-europeana-cased/ \
--tokenizer_name /mnt/datasets/bert-base-finnish-europeana-cased/ \
--train_file /mnt/datasets/hlms/extracted_content_Finnish_0.6.txt \
--validation_file /mnt/datasets/hlms/finnish_validation.txt \
--max_seq_length 512 \
--per_device_train_batch_size 16 \
--learning_rate 1e-4 \
--num_train_epochs 40 \
--preprocessing_num_workers 96 \
--output_dir /mnt/datasets/bert-base-finnish-europeana-cased-512-dupe1-noadafactor-40e \
--save_steps 2500 \
--eval_steps 2500 \
--warmup_steps 10000 \
--line_by_line \
--pad_to_max_length

The following plot shows the pretraining loss curve:

Swedish model

The BERT model - with texts from Swedish part of Europeana - was trained with the Hugging Face JAX/FLAX implementation for 40 epochs (approx. 660K steps) on a v3-8 TPU, using the following command:

python3 run_mlm_flax.py --model_type bert \
--config_name /mnt/datasets/bert-base-swedish-europeana-cased/ \
--tokenizer_name /mnt/datasets/bert-base-swedish-europeana-cased/ \
--train_file /mnt/datasets/hlms/extracted_content_Swedish_0.6.txt \
--validation_file /mnt/datasets/hlms/swedish_validation.txt \
--max_seq_length 512 \
--per_device_train_batch_size 16 \
--learning_rate 1e-4 \
--num_train_epochs 40 \
--preprocessing_num_workers 96 \
--output_dir /mnt/datasets/bert-base-swedish-europeana-cased-512-dupe1-noadafactor-40e \
--save_steps 2500 \
--eval_steps 2500 \
--warmup_steps 10000 \
--line_by_line \
--pad_to_max_length

The following plot shows the pretraining loss curve:

Acknowledgments

Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC) program, previously known as TensorFlow Research Cloud (TFRC). Many thanks for providing access to the TRC ❤️

Thanks to the generous support from the Hugging Face team, it is possible to download both cased and uncased models from their S3 storage 🤗