readme: add initial version

README.md

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+---
+language: english 
+license: mit
+widget:
+- text: "and I cannot conceive the reafon why [MASK] hath"
+---
+
+# 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](http://www.europeana-newspapers.eu/)       | 13-28GB (filtered)
+| French   | [Europeana](http://www.europeana-newspapers.eu/)       | 11-31GB (filtered)
+| English  | [British Library](https://data.bl.uk/digbks/db14.html) | 24GB (year filtered)
+| Finnish  | [Europeana](http://www.europeana-newspapers.eu/)       | 1.2GB
+| Swedish  | [Europeana](http://www.europeana-newspapers.eu/)       | 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](https://huggingface.co/dbmdz/bert-base-historic-multilingual-cased)
+| `dbmdz/bert-base-historic-english-cased`      | [here](https://huggingface.co/dbmdz/bert-base-historic-english-cased)
+| `dbmdz/bert-base-finnish-europeana-cased`     | [here](https://huggingface.co/dbmdz/bert-base-finnish-europeana-cased)
+| `dbmdz/bert-base-swedish-europeana-cased`     | [here](https://huggingface.co/dbmdz/bert-base-swedish-europeana-cased)
+
+# 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:
+
+![German Europeana Corpus Stats](stats/figures/german_europeana_corpus_stats.png)
+
+## 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:
+
+![French Europeana Corpus Stats](stats/figures/french_europeana_corpus_stats.png)
+
+## British Library Corpus
+
+Metadata is taken from [here](https://data.bl.uk/digbks/DB21.html). 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:
+
+![British Library Corpus Stats](stats/figures/bl_corpus_stats.png)
+
+## Finnish Europeana Corpus
+
+| OCR confidence | Size
+| -------------- | ----
+| 0.60           | 1.2GB
+
+The following plot shows a tokens per year distribution:
+
+![Finnish Europeana Corpus Stats](stats/figures/finnish_europeana_corpus_stats.png)
+
+## Swedish Europeana Corpus
+
+| OCR confidence | Size
+| -------------- | ----
+| 0.60           | 1.1GB
+
+The following plot shows a tokens per year distribution:
+
+![Swedish Europeana Corpus Stats](stats/figures/swedish_europeana_corpus_stats.png)
+
+## All Corpora
+
+The following plot shows a tokens per year distribution of the complete training corpus:
+
+![All Corpora Stats](stats/figures/all_corpus_stats.png)
+
+# 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:
+
+```bash
+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:
+
+![Training loss curve](stats/figures/pretraining_loss_historic-multilingual.png)
+
+## 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:
+
+```bash
+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:
+
+![Training loss curve](stats/figures/pretraining_loss_historic_english.png)
+
+## 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:
+
+```bash
+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:
+
+![Training loss curve](stats/figures/pretraining_loss_finnish_europeana.png)
+
+## 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:
+
+```bash
+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:
+
+![Training loss curve](stats/figures/pretraining_loss_swedish_europeana.png)
+
+# 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](https://huggingface.co/) team,
+it is possible to download both cased and uncased models from their S3 storage 🤗