dbmdz/bert-base-historic-multilingual-cased

hmBERT: Historical Multilingual Language Models for Named Entity Recognition

More information about our hmBERT model can be found in our new paper: "hmBERT: Historical Multilingual Language Models for Named Entity Recognition".

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

Smaller Models

We have 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

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.

Pretraining

Multilingual model

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:

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 🤗