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README.md ADDED
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+ ---
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+ language: english
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+ license: mit
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+ widget:
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+ - text: "and I cannot conceive the reafon why [MASK] hath"
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+ ---
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+
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+ # Historic Language Models (HLMs)
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+
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+ ## Languages
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+
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+ Our Historic Language Models Zoo contains support for the following languages - incl. their training data source:
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+
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+ | Language | Training data | Size
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+ | -------- | ------------- | ----
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+ | German | [Europeana](http://www.europeana-newspapers.eu/) | 13-28GB (filtered)
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+ | French | [Europeana](http://www.europeana-newspapers.eu/) | 11-31GB (filtered)
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+ | English | [British Library](https://data.bl.uk/digbks/db14.html) | 24GB (year filtered)
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+ | Finnish | [Europeana](http://www.europeana-newspapers.eu/) | 1.2GB
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+ | Swedish | [Europeana](http://www.europeana-newspapers.eu/) | 1.1GB
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+
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+ ## Models
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+
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+ At the moment, the following models are available on the model hub:
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+
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+ | Model identifier | Model Hub link
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+ | --------------------------------------------- | --------------------------------------------------------------------------
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+ | `dbmdz/bert-base-historic-multilingual-cased` | [here](https://huggingface.co/dbmdz/bert-base-historic-multilingual-cased)
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+ | `dbmdz/bert-base-historic-english-cased` | [here](https://huggingface.co/dbmdz/bert-base-historic-english-cased)
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+ | `dbmdz/bert-base-finnish-europeana-cased` | [here](https://huggingface.co/dbmdz/bert-base-finnish-europeana-cased)
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+ | `dbmdz/bert-base-swedish-europeana-cased` | [here](https://huggingface.co/dbmdz/bert-base-swedish-europeana-cased)
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+
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+ # Corpora Stats
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+
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+ ## German Europeana Corpus
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+
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+ We provide some statistics using different thresholds of ocr confidences, in order to shrink down the corpus size
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+ and use less-noisier data:
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+
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+ | OCR confidence | Size
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+ | -------------- | ----
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+ | **0.60** | 28GB
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+ | 0.65 | 18GB
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+ | 0.70 | 13GB
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+
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+ For the final corpus we use a OCR confidence of 0.6 (28GB). The following plot shows a tokens per year distribution:
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+
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+ ![German Europeana Corpus Stats](stats/figures/german_europeana_corpus_stats.png)
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+
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+ ## French Europeana Corpus
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+
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+ Like German, we use different ocr confidence thresholds:
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+
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+ | OCR confidence | Size
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+ | -------------- | ----
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+ | 0.60 | 31GB
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+ | 0.65 | 27GB
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+ | **0.70** | 27GB
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+ | 0.75 | 23GB
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+ | 0.80 | 11GB
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+
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+ For the final corpus we use a OCR confidence of 0.7 (27GB). The following plot shows a tokens per year distribution:
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+
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+ ![French Europeana Corpus Stats](stats/figures/french_europeana_corpus_stats.png)
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+
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+ ## British Library Corpus
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+
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+ Metadata is taken from [here](https://data.bl.uk/digbks/DB21.html). Stats incl. year filtering:
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+
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+ | Years | Size
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+ | ----------------- | ----
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+ | ALL | 24GB
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+ | >= 1800 && < 1900 | 24GB
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+
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+ We use the year filtered variant. The following plot shows a tokens per year distribution:
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+
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+ ![British Library Corpus Stats](stats/figures/bl_corpus_stats.png)
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+
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+ ## Finnish Europeana Corpus
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+
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+ | OCR confidence | Size
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+ | -------------- | ----
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+ | 0.60 | 1.2GB
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+
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+ The following plot shows a tokens per year distribution:
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+
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+ ![Finnish Europeana Corpus Stats](stats/figures/finnish_europeana_corpus_stats.png)
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+
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+ ## Swedish Europeana Corpus
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+
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+ | OCR confidence | Size
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+ | -------------- | ----
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+ | 0.60 | 1.1GB
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+
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+ The following plot shows a tokens per year distribution:
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+
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+ ![Swedish Europeana Corpus Stats](stats/figures/swedish_europeana_corpus_stats.png)
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+
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+ ## All Corpora
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+
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+ The following plot shows a tokens per year distribution of the complete training corpus:
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+
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+ ![All Corpora Stats](stats/figures/all_corpus_stats.png)
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+
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+ # Multilingual Vocab generation
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+
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+ For the first attempt, we use the first 10GB of each pretraining corpus. We upsample both Finnish and Swedish to ~10GB.
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+ The following tables shows the exact size that is used for generating a 32k and 64k subword vocabs:
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+
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+ | Language | Size
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+ | -------- | ----
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+ | German | 10GB
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+ | French | 10GB
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+ | English | 10GB
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+ | Finnish | 9.5GB
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+ | Swedish | 9.7GB
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+
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+ We then calculate the subword fertility rate and portion of `[UNK]`s over the following NER corpora:
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+
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+ | Language | NER corpora
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+ | -------- | ------------------
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+ | German | CLEF-HIPE, NewsEye
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+ | French | CLEF-HIPE, NewsEye
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+ | English | CLEF-HIPE
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+ | Finnish | NewsEye
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+ | Swedish | NewsEye
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+
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+ Breakdown of subword fertility rate and unknown portion per language for the 32k vocab:
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+
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+ | Language | Subword fertility | Unknown portion
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+ | -------- | ------------------ | ---------------
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+ | German | 1.43 | 0.0004
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+ | French | 1.25 | 0.0001
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+ | English | 1.25 | 0.0
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+ | Finnish | 1.69 | 0.0007
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+ | Swedish | 1.43 | 0.0
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+
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+ Breakdown of subword fertility rate and unknown portion per language for the 64k vocab:
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+
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+ | Language | Subword fertility | Unknown portion
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+ | -------- | ------------------ | ---------------
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+ | German | 1.31 | 0.0004
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+ | French | 1.16 | 0.0001
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+ | English | 1.17 | 0.0
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+ | Finnish | 1.54 | 0.0007
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+ | Swedish | 1.32 | 0.0
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+
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+ # Final pretraining corpora
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+
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+ We upsample Swedish and Finnish to ~27GB. The final stats for all pretraining corpora can be seen here:
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+
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+ | Language | Size
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+ | -------- | ----
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+ | German | 28GB
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+ | French | 27GB
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+ | English | 24GB
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+ | Finnish | 27GB
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+ | Swedish | 27GB
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+
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+ Total size is 130GB.
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+
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+ # Pretraining
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+
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+ ## Multilingual model
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+
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+ We train a multilingual BERT model using the 32k vocab with the official BERT implementation
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+ on a v3-32 TPU using the following parameters:
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+
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+ ```bash
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+ python3 run_pretraining.py --input_file gs://histolectra/historic-multilingual-tfrecords/*.tfrecord \
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+ --output_dir gs://histolectra/bert-base-historic-multilingual-cased \
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+ --bert_config_file ./config.json \
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+ --max_seq_length=512 \
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+ --max_predictions_per_seq=75 \
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+ --do_train=True \
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+ --train_batch_size=128 \
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+ --num_train_steps=3000000 \
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+ --learning_rate=1e-4 \
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+ --save_checkpoints_steps=100000 \
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+ --keep_checkpoint_max=20 \
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+ --use_tpu=True \
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+ --tpu_name=electra-2 \
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+ --num_tpu_cores=32
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+ ```
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+
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+ The following plot shows the pretraining loss curve:
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+
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+ ![Training loss curve](stats/figures/pretraining_loss_historic-multilingual.png)
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+
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+ ## English model
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+
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+ The English BERT model - with texts from British Library corpus - was trained with the Hugging Face
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+ JAX/FLAX implementation for 10 epochs (approx. 1M steps) on a v3-8 TPU, using the following command:
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+
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+ ```bash
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+ python3 run_mlm_flax.py --model_type bert \
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+ --config_name /mnt/datasets/bert-base-historic-english-cased/ \
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+ --tokenizer_name /mnt/datasets/bert-base-historic-english-cased/ \
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+ --train_file /mnt/datasets/bl-corpus/bl_1800-1900_extracted.txt \
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+ --validation_file /mnt/datasets/bl-corpus/english_validation.txt \
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+ --max_seq_length 512 \
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+ --per_device_train_batch_size 16 \
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+ --learning_rate 1e-4 \
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+ --num_train_epochs 10 \
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+ --preprocessing_num_workers 96 \
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+ --output_dir /mnt/datasets/bert-base-historic-english-cased-512-noadafactor-10e \
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+ --save_steps 2500 \
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+ --eval_steps 2500 \
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+ --warmup_steps 10000 \
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+ --line_by_line \
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+ --pad_to_max_length
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+ ```
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+
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+ The following plot shows the pretraining loss curve:
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+
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+ ![Training loss curve](stats/figures/pretraining_loss_historic_english.png)
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+
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+ ## Finnish model
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+
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+ The BERT model - with texts from Finnish part of Europeana - was trained with the Hugging Face
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+ JAX/FLAX implementation for 40 epochs (approx. 1M steps) on a v3-8 TPU, using the following command:
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+
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+ ```bash
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+ python3 run_mlm_flax.py --model_type bert \
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+ --config_name /mnt/datasets/bert-base-finnish-europeana-cased/ \
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+ --tokenizer_name /mnt/datasets/bert-base-finnish-europeana-cased/ \
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+ --train_file /mnt/datasets/hlms/extracted_content_Finnish_0.6.txt \
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+ --validation_file /mnt/datasets/hlms/finnish_validation.txt \
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+ --max_seq_length 512 \
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+ --per_device_train_batch_size 16 \
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+ --learning_rate 1e-4 \
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+ --num_train_epochs 40 \
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+ --preprocessing_num_workers 96 \
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+ --output_dir /mnt/datasets/bert-base-finnish-europeana-cased-512-dupe1-noadafactor-40e \
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+ --save_steps 2500 \
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+ --eval_steps 2500 \
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+ --warmup_steps 10000 \
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+ --line_by_line \
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+ --pad_to_max_length
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+ ```
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+
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+ The following plot shows the pretraining loss curve:
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+
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+ ![Training loss curve](stats/figures/pretraining_loss_finnish_europeana.png)
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+
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+ ## Swedish model
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+
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+ The BERT model - with texts from Swedish part of Europeana - was trained with the Hugging Face
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+ JAX/FLAX implementation for 40 epochs (approx. 660K steps) on a v3-8 TPU, using the following command:
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+
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+ ```bash
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+ python3 run_mlm_flax.py --model_type bert \
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+ --config_name /mnt/datasets/bert-base-swedish-europeana-cased/ \
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+ --tokenizer_name /mnt/datasets/bert-base-swedish-europeana-cased/ \
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+ --train_file /mnt/datasets/hlms/extracted_content_Swedish_0.6.txt \
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+ --validation_file /mnt/datasets/hlms/swedish_validation.txt \
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+ --max_seq_length 512 \
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+ --per_device_train_batch_size 16 \
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+ --learning_rate 1e-4 \
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+ --num_train_epochs 40 \
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+ --preprocessing_num_workers 96 \
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+ --output_dir /mnt/datasets/bert-base-swedish-europeana-cased-512-dupe1-noadafactor-40e \
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+ --save_steps 2500 \
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+ --eval_steps 2500 \
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+ --warmup_steps 10000 \
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+ --line_by_line \
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+ --pad_to_max_length
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+ ```
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+
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+ The following plot shows the pretraining loss curve:
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+
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+ ![Training loss curve](stats/figures/pretraining_loss_swedish_europeana.png)
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+
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+ # Acknowledgments
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+
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+ Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC) program, previously known as
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+ TensorFlow Research Cloud (TFRC). Many thanks for providing access to the TRC ❤️
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+
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+ Thanks to the generous support from the [Hugging Face](https://huggingface.co/) team,
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+ it is possible to download both cased and uncased models from their S3 storage 🤗
stats/figures/all_corpus_stats.png ADDED
stats/figures/bl_corpus_stats.png ADDED
stats/figures/finnish_europeana_corpus_stats.png ADDED
stats/figures/french_europeana_corpus_stats.png ADDED
stats/figures/german_europeana_corpus_stats.png ADDED
stats/figures/pretraining_loss_finnish_europeana.png ADDED
stats/figures/pretraining_loss_historic-multilingual.png ADDED
stats/figures/pretraining_loss_historic_english.png ADDED
stats/figures/pretraining_loss_swedish_europeana.png ADDED
stats/figures/swedish_europeana_corpus_stats.png ADDED